WO2021209963A1

WO2021209963A1 - A computer-implemented system and method for determining an optimal and resilient configuration of process units

Info

Publication number: WO2021209963A1
Application number: PCT/IB2021/053150
Authority: WO
Inventors: Karthick RAMALINGAM; Nandakumar Velayudhan Pillai; Shyamprasad KAMATH; Sanjay Varma; Vinayakumar MUNDANAT
Original assignee: Mangalore Refinery & Petrochemicals Ltd.
Priority date: 2020-04-16
Filing date: 2021-04-16
Publication date: 2021-10-21

Abstract

The present disclosure relates to the field of computational optimization and discloses a computer-implemented method(200) for determining an optimal and resilient configuration of process units. The method(200) comprises modelling and solving(202,204) a configuration optimization problem to determine a set of profit-optimized configurations; evaluating(206) a first profit measure associated with each of the profit-optimized configurations; plugging(208) the determined first profit measure and a cost of investment towards each configuration into a financial model; determining(210) values associated with a plurality of financial parameters for each profit-optimized configuration based on corresponding first profit measure and cost of investment; selecting(212) a set of optimal configurations based on the financial parameters; and subjecting(214) the selected optimal configurations to a configuration stress test and resilience analysis (CONSTRA) to determine at least one optimal and resilient configuration of process units. The optimal and resilient configuration has a maximum resilience against unfavorable events on its profitability.

Description

A COMPUTER-IMPLEMENTED SYSTEM AND METHOD FOR DETERMINING AN OPTIMAL AND RESILIENT CONFIGURATION OF PROCESS UNITS

FIELD The present disclosure relates to optimization of process unit configuration. More particularly, the present disclosure relates to a computer-implemented system and method for determining an optimal and resilient configuration of process units in a complex chemical/ process industry.

DEFINITIONS As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used indicate otherwise.

Configuration stress test - The configuration stress test is a review of chemical plants being conducted in light of the unfortunate events. The test comprises a review of plant design and an assessment of robustness. The review is based on different scenarios, or priority issues, which are reviewed independently of the probability of occurrence.

Resilience analysis - The resilience analysis of a system is related to analyzing its ability to withstand stressors, adapt, and rapidly recover from disruptions. Two significant challenges of resilience analysis are to (1) quantify the resilience associated with a given recovery curve; and (2) develop a rigorous mathematical model of the recovery process.

Financial Model - The financial model is a mathematical model designed to represent the performance of a financial asset or portfolio of a business, project, or any other investment.

BACKGROUND

The background information herein below relates to the present disclosure but is not necessarily prior art.

The chemicals/process industry is a very capital-intensive industry. Currently, the capital expenditure (CapEx) of world-scale integrated petroleum refining and petrochemical complexes are in billions of US dollar. The magnitude and the project life of such complexes can be typically up to fifteen years. Hence, it is essential to carry out configuration or process network design of process industries in in judicious manner.

Conventionally, configuration or network design of process units in complex chemical/process industries such as petroleum refineries, petrochemicals etc. are arrived by optimization programs such as: linear programming, non-linear programming, mixed integer linear programming and mixed integer non-linear programming.

One or more commercial applications such as Aspen Process Industry Modeling System (Aspen PIMS), Generalized Refining Transportation Marketing Planning System (GRTMPS), Refining and Petrochemical Modeling System (RPMS) etc. are used for building the complex-wide models for the aforesaid optimization.

Typically, raw-material grades and volume range along with their costs, product grades and desirable volume range are used as inputs in the optimization application. All possible technologies are modeled as sub-models along with the networking flexibility in the process units’ complex to arrive at an optimal network design. The optimal design capacities of process units and their feedstock mix are hence arrived.

The limitation with the above approach is that although the resultant configuration is optimal, it lacks flexibility and resilience in margins. This is due to the fact that there is no design methodology to introduce flexibility in the configuration and hence shutdown of one crucial process unit may force shutdown of the entire complex. Traditionally, researchers have attempted to evaluate the risks due to feedstock supply chain disruption during operation of a process industry configuration. In such cases as the configuration is already frozen, these studies quantitatively estimate the financial risk associated with the configuration and limit the risk evaluation to that of supply-chain disruptions. The whole problem is approached in evaluating the operational risk of a configuration designed and constructed already.

The existing literature is not focused on the subjects such as design of a configuration that is elastic to disruptions, requirement of further investments to infuse resilience, study on whether such resilience schemes are worth investing or is there a configuration which could have handled such disruptions better. In one of the prior art literatures (Gandhi, Kortnicki, & Nangia, titled “RAM analysis for refinery process design optimization”, hydrocarbon processing, dated January 2020), the sensitivity of a process industry configuration to Reliability, Availability and Maintainability (RAM) alone has been studied so as to design intermediate storage tanks volume. However, none of the conventional configuration design methodologies talk about subjecting selected configurations to different profit stressing aspects during configuration study or design, so as to arrive at the best configuration.

Further, conventional configuration design methodologies follow a two-stage stochastic approach operational optimization of an established configuration. In such cases, the stressors are assumed to be stochastic in nature and are introduced in the second stage of Mixed Integer Linear Programming (MILP)/ Mixed Integer Non-Linear Programming (MINLP). However, stochastic processes subject the configurations to random noise, which is not recommended.

Therefore, there is felt a need for a computer-implemented system and method for determining an optimal and resilient configuration of process units that alleviates the aforementioned drawbacks.

OBJECTS

Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as follows: An object of the present disclosure is to provide a computer-implemented system and method for determining an optimal and resilient configuration of process units in a complex chemical/ process industry.

Another object of the present disclosure is to provide a computer-implemented system and method for determining an optimal and resilient configuration of process units that is cost- effective.

Still another object of the present disclosure is to provide a computer-implemented system and method to arrive at the best configuration or network of process units with maximum elasticity or resilience against unfavorable events on the maximized profitability of the configuration. Yet another object of the present disclosure is to provide a computer-implemented system and method that weighs in the cost against the benefits for infusing the resilience and recommends a data driven decision against the resilience investments.

Still another object of the present disclosure is to provide a computer-implemented system and method for determining an optimal and resilient configuration of process units that uses deterministic approach for evaluation of financial impact of stress on process unit configurations.

Yet another object of the present disclosure is to provide a computer-implemented system and method that examines the impact of stressors on the configurations design, determines the resilience boosters required to mitigate the same, and their corresponding investment so as to rightly decide on the investment required to boost the resilience versus its benefit.

Still another object of the present disclosure is to provide a computer-implemented system and method that examines the disruption due to feedstock cost or product price on a process industry configuration.

Other objects and advantages of the present disclosure will be more apparent from the following description when read in conjunction with the accompanying figures, which are not intended to limit the scope of the present disclosure.

SUMMARY

The present disclosure envisages a computer-implemented method for determining an optimal and resilient configuration of process units in a complex process plant. The method comprises modelling, using a solver, a configuration optimization problem to maximize a total profit of a network of the process units subject to a pre-defined set of constraints; solving, using the solver, the configuration optimization problem to determine a set of profit- optimized configurations of the process units; evaluating, by the solver, a first profit measure associated with each of the profit-optimized configurations; plugging, by the solver, the determined first profit measure and a cost of investment for each profit-optimized configuration into a financial model; determining, by the financial model, a first set of values associated with a plurality of financial parameters for each of the profit-optimized configurations based on corresponding first profit measure and cost of investment; selecting, by the financial model, a set of optimal configurations from the profit-optimized configurations based on the financial parameters; and subjecting, by a computation engine, the selected optimal configurations to a configuration stress test and resilience analysis (CONSTRA) to determine at least one optimal and resilient configuration of process units, wherein the optimal and resilient configuration has a maximum resilience against unfavorable events on the profitability thereof.

The constraints can comprise overall material balance constraints, feed mass balance constraints, process unit capacity constraints, process unit’s product property relation constraints, product blending mass balance constraints, internal fuel mass balance constraints, loss mass balance constraints, overall utility balance constraints, internal utility balance constraints, utility unit capacity constraints, final product blend’s property constraints, blend specification constraints, feedstock availability constraints, and product demand constraints.

The financial parameters can be selected from the group consisting of Internal Rate of Return (IRR), Net Present Value (NPV), Payback Period (PBP), and Profitability Index (PI).

In an embodiment, the step of subjecting each of the selected optimal configurations to the configuration stress test and resilience analysis comprises applying, by the computation engine, a plurality of pre-determined stressors on each of the selected optimal configurations; identifying, by the computation engine, a set of mitigation measures required to infuse resilience against the stressors in each of the selected optimal configurations; estimating, by the computation engine, capital costs required for introducing each of the identified mitigation measures; sending, by the computation engine, the stressors applied on each configuration to the solver to facilitate calculation of an annualized loss corresponding to each stressor for the selected optimal configurations; facilitating, by the computation engine, calculation of a set of indices based on the calculated annualized loss of each stressed configuration and the capital costs associated with the identified mitigation measures; and analyzing, by the computation engine, the evaluated indices to arrive at the optimal and resilient configuration of process units.

The stressors applied can include stressors relative to an optimal maximized margin during configuration design due to at least one of disruptions in feedstock or product price, seasonality or cyclicity in feedstock availability or product demand, reliability of the individual process units, failure in feedstock supply or product dispatch mode, introduction of superior product specification, and introduction of new statutory norm concerning the operation. The stressors can be modeled as binary events, deterministic functions, or probability distribution functions. In an embodiment, step of sending the stressors applied on each configuration to the solver to facilitate calculation of the annualized loss corresponding to each stressor for the selected optimal configurations comprises determining, by the solver, a second profit measure for each of the selected optimal configurations for each stressor; determining, by the solver, a total loss potential for each stressor based on the difference between the first profit measure and the second profit measure; and multiplying, by the solver, a probability of stressor’s occurrence in a year’s time with the determined total loss potential to calculate the annualized loss corresponding to each stressor for the selected optimal configurations.

Similarly, the step of facilitating calculation of the set of indices based on the calculated annualized loss of each stressed configuration and the capital costs associated with identified mitigation measures comprises receiving, by the financial model, the estimated capital costs associated with each of the mitigation measures; evaluating, by the financial model, a second set of values associated with the financial parameters for each of the selected optimal configurations; receiving, by the computation engine, the evaluated second set of values associated with the financial parameters; computing, by the computation engine, a capex stressor component (CSC) associated with the mitigation measures based on a difference between the first set of values and the second set of values associated with the financial parameters; computing, by the computation engine, a capex stressor component index associated with the mitigation measures based on the capex stressor component (CSC) and a pre-defined base value; plugging, by the solver, the calculated annualized loss of each stressed configuration into the financial model to determine a third set of values associated with the financial parameters; computing, by the computation engine, a margin stressor component (MSC) associated with the mitigation measures based on the first set of values and the third set of values associated with the financial parameters; and computing, by the computation engine, a margin stressor component index associated with the mitigation measures based on the margin stressor component (MSC) and the pre-defined base value.

In an embodiment, the base value is a difference between the first set of values associated with the financial parameters and a set of corresponding minimum acceptable values. In another embodiment, the base value is a difference between the first set of values associated with the financial parameters and a corresponding lowest set of second and third values associated with the financial parameters. In an embodiment, the step of analyzing, by the computation engine, the evaluated indices to arrive at the optimal and resilient configuration comprises classifying, by the computation engine, the evaluated capex stressor component and margin stressor component indices associated with each of the stressed configurations into four classes to generate a Resilience Investment Intensity (RII) matrix; generating, by the computation engine, a set of recommended actions indicative of configuration modifications to be implemented to infuse resilience against the stressors based on the generated RII matrix; computing, by the computation engine, an increase in project investment cost for each of the configuration modifications; plugging, by the solver, the computed increased investment cost in the financial model to facilitate computation of a fourth set of values associated with the financial parameters based on the increased investment costs; ranking, by the computation engine, the modified configurations based on the fourth set of values associated with the financial parameters to arrive at the optimal and resilient configuration with maximum resilience against unfavorable events on the profitability thereof. Advantageously, the solver is a mixed-integer nonlinear programming (MINLP) solver.

The present disclosure further envisages a computer-implemented system for determining an optimal and resilient configuration of process units in a complex process plant.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING

A computer-implemented system and method for determining an optimal and resilient configuration of process units in a complex process plant will now be described with the help of the accompanying drawings, in which:

Figure 1 illustrates a block diagram of a computer-implemented system for determining an optimal and resilient configuration of process units in a complex process plant, in accordance with the present disclosure; Figures 2a and 2b illustrate a flow diagram of a computer-implemented method for determining an optimal and resilient configuration of process units in a complex process plant, in accordance with the present disclosure;

Figure 3 illustrates an exemplary Resilience Investment Intensity (RII) matrix, in accordance with the system and method of the present disclosure; Figure 4 illustrates an exemplary RII investment decision tree (RIDT), in accordance with the method of Figures 2a and 2b of the present disclosure;

Figure 5 illustrates an exemplary Resilience Investment Intensity (RII) matrix generated for selected configuration(s) as a result of experimentation, in accordance with the system and method of the present disclosure; and

Figures 6a and 6b illustrate a flow chart depicting implementation logic of the method of Figures 2a and 2b, in accordance with the present disclosure.

LIST OF REFERENCE NUMERALS

100 - System 102 - Memory

102a - Financial model 104 - Solver 104a - Interface 104b - Optimizer 104c - First computation module

104d - Second computation module 104e - Selection module 106 - Computation engine 106a - Stress induction module 106b - Identification module

106c - Third computation module 106d - Fourth computation module

106e - Index calculation module 106f - Analyzing module

DETAILED DESCRIPTION

Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.

Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.

The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms “a”, “an”, and “the” may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms “comprises”, “comprising”, “including”, and “having”, are open ended transitional phrases and therefore specify the presence of stated features, elements, modules, units and/or components, but do not forbid the presence or addition of one or more other features, elements, components, and/or groups thereof.

When an element is referred to as being “mounted on”, “engaged to”, “connected to”, or “coupled to” another element, it may be directly on, engaged, connected or coupled to the other element. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed elements.

The terms first, second, third, etc., should not be construed to limit the scope of the present disclosure as the aforementioned terms may be only used to distinguish one element, component, region, or section from another element, component, region, or section. Terms such as first, second, third etc., when used herein do not imply a specific sequence or order unless clearly suggested by the present disclosure.

Process Industries such as petroleum refining and petrochemical complex, biorefining complex etc. consist of a network of different process units and these combination of process units is named as its configuration. Configuration design of process industries is usually arrived by optimization methodologies such as Mixed Integer Linear Programming (MILP) or Mixed Integer Non-Linear Programming (MINLP). Most of the conventional optimization methodologies for process unit configurations only evaluate stress due to the supply chain risks during a configuration’s operation. Further, some traditional methodologies relate to evaluation of the sensitivity of a process industry configuration to impact on Reliability, Availability and Maintainability (RAM) so as to design intermediate storage tanks volume. However, during configuration study or design stage, a selected configurations’ sensitivity to all the profit stressing aspects such as disruptions in feedstock or product price, seasonality or cyclicity in feedstock availability or product demand, reliability of the individual process units, failure in feedstock supply or product dispatch mode, introduction of superior product specification, introduction of new statutory norm concerning the operation and the like are not known to have been studied holistically so as to arrive at the best configuration.

In order to alleviate the aforementioned shortcomings of the existing optimization methodologies, a computer-implemented system (hereinafter referred to as “system 100”) and method (hereinafter referred to as “method 200”) for determining an optimal and resilient configuration of process units are now being described with reference to Figure 1 through Figure 6b.

Referring to Figures 1, 2a and 6a, the computer-implemented method 200 comprises the following steps:

At step 202, a configuration optimization problem is modeled using a solver 104 to maximize a total profit of a network of the process units subject to a pre-defined set of constraints.

Optimization techniques such as Mixed Integer Linear Programming (MILP) or Mixed Integer Non-Linear Programming (MINLP) may be used for modeling the optimization problem.

An example of model formulation is provided below:

Every process unit complex configuration’s objective will be to maximize the total profit derived from the network of process units and therefore the objective function is:

Maximize

Where MP; = market price per unit mass of any product T such that T G {0,1,2,...i}

P; = mass flow rate of any product ‘i’

PC_j = purchase cost per unit mass of any feed ‘j’ such that ‘j’ G {0,1,2,...j }

F_j = mass flow rate of any feed ‘j ’

U_c,imp = energy rate of any utility stream ‘c’ imported from source ‘imp’ such that ‘c’ G {0,l,2,...c} and ‘imp’ G {0,1,2, ...imp}

I_c.i_mp = unit energy price of any utility stream ‘c’ imported from source ‘imp’

U_c,exp = energy rate of any utility stream ‘c’ exported to destination ‘exp’ such that ‘exp’ G {0,1,2, _ b}

O_c,exp = unit energy price of any utility stream ‘c’ exported to destination ‘exp’

VC i = any other variable costs like catalyst, chemicals etc., for a given process unit ‘k’ such that ‘k’ C {0,l,2,...k}

FC = Aggregate of all other fixed costs like but not limited to salaries and wages etc.

The optimization problem may be subject to constraints such as, but not limited to, overall material balance constraints, feed mass balance constraints, process unit capacity constraints, process unit’s product property relation constraints, product blending mass balance constraints, internal fuel mass balance constraints, loss mass balance constraints, overall utility balance constraints, internal utility balance constraints, utility unit capacity constraints, final product blend’s property constraints, blend specification constraints, feedstock availability constraints, and product demand constraints.

The above example of model formulation is a very simplified way of depicting the configuration optimization problem. Integer programming to avoid teaspoon blending, special order set functionality for selecting the operating mode of a process unit etc. are not depicted in the above problem for the sake of brevity. The configuration problems may be modeled using standard software applications like General Algebraic Modeling System (GAMS), Frontline Systems MS Excel based application, and the like.

At step 204, the configuration optimization problem is solved using the solver 104 to determine a set of profit-optimized configurations of the process units.

At step 206, the solver 104 evaluates a first profit measure associated with each of the profit- optimized configurations. At step 208, the solver 104 plugs in the determined first profit measure and a cost of investment towards each profit-optimized configuration into a financial model 102a. The cost of investment towards each configuration may be computed based on the profit-optimized configurations and the capacities derived using the solver.

At step 210, the financial model 102a determines a first set of values associated with a plurality of financial parameters for each of the profit-optimized configurations based on corresponding first profit measure and cost of investment. The financial parameters may be selected from the group consisting of, but not limited to, Internal Rate of Return (IRR), Net Present Value (NPV), Payback Period (PBP), and Profitability Index (PI). The financial parameter selected can be any other appropriate indicator of economic value generated by the investment throughout its life.

At step 212, the financial model 102a selects a set of optimal configurations from the profit- optimized configurations based on the financial parameters. The optimal configurations are the best ranking configurations selected from various configuration options by arranging the configurations in the descending order of their financial parameters.

At step 214, a computation engine 106 subjects the selected optimal configurations to a configuration stress test and resilience analysis (CONSTRA) to determine at least one optimal and resilient configuration of process units. The optimal and resilient configuration has a maximum resilience against unfavorable events on the profitability thereof.

In an embodiment, referring to Figure 2b, the step 214 of subjecting each of the selected optimal configurations to the configuration stress test and resilience analysis comprises the following sub-steps -

At step 214a, the computation engine 106 applies a plurality of pre-determined stressors on each of the selected optimal configurations. Stressors are defined as unfavorable events or occurrences which can result in suboptimal operation of the process units’ network and hence restrain the configuration from deriving its best profit. The stressors can include, but are not limited to, at least one of disruptions in feedstock or product price, seasonality or cyclicity in feedstock availability or product demand, reliability of the individual process units, failure in feedstock supply or product dispatch mode, introduction of superior product specification, and introduction of new statutory norm concerning the operation. At step 214b, the computation engine 106 identifies a set of mitigation measures (also referred to as ‘resilience boosters’) required to infuse resilience against the stressors in each of the selected optimal configurations.

At step 214c, the computation engine 106 estimates the capital costs required for introducing each of the identified mitigation measures. The capital costs of mitigation measures correspond to the additional investment required to implement the measures. This resilience investment does not add to the profit derived by optimal operation of a given configuration and hence may not fetch any incentive on financial parameters like IRR on the project capex, however it minimizes the loss in margin when the stressor acts on the configuration or network of process units. The capital costs may be estimated based on standard/market prices of additional component(s) or modification work required to be performed for booting resilience or for implementing the mitigation measures.

Thus, reduction in financial parameters such as IRR is differentiated into the following two components: due to increase in project capital cost, and due to decrease in the profit or margin.

At step 214d, the computation engine 106 sends stressors applied on each configuration to the solver 104 to facilitate calculation of an annualized loss corresponding to each stressor for the selected optimal configurations.

At step 214e, the computation engine 106 facilitates calculation of a set of indices based on the calculated annualized loss of each stressed configuration and the capital costs associated with the identified mitigation measures.

At step 214f, the computation engine 106 analyzes the evaluated indices to arrive at the optimal and resilient configuration of process units.

Though the stressors can be modelled as a stochastic processes, they are modelled either as binary events, deterministic functions, or probability distribution functions. This is because the stressors in cost or prices are recommended to be time series decomposed so as to eliminate random noise. The trend of time series gives insight on crust and trough of the prices, cyclicity of the prices, etc. Deterministic approach enables weighing cost versus benefit analysis of investments necessary for resilience improvement. In an embodiment, the step 214d of sending the stressors applied on each configuration to the solver 104 to facilitate calculation of the annualized loss corresponding to each stressor for the selected optimal configurations comprises: determining, by the solver 104, a second profit measure for each of the selected optimal configurations for each stressor; determining, by the solver 104, a total loss potential for each stressor based on the difference between the first profit measure and the second profit measure; and multiplying, by the solver 104, a probability of stressor’s occurrence in a year’s time with the determined total loss potential to calculate the annualized loss corresponding to each stressor for the selected optimal configurations.

The probability of occurrence of stressor may be determined from historical trend data, data hank from a third party and the like.

The solver may be a mixed-integer nonlinear programming (MINLP) solver.

In an embodiment, the step 214e of facilitating calculation of the set of indices based on the calculated annualized loss of each stressed configuration and the capital costs associated with identified mitigation measures comprises: receiving, by the financial model 102a, the estimated capital costs associated with each of the mitigation measures; evaluating, by the financial model 102a, a second set of values associated with the financial parameters for each of the selected optimal configurations; receiving, by the computation engine 106, the evaluated second set of values associated with the financial parameters from the financial model 102a; computing, by the computation engine 106, a capex stressor component (CSC) associated with the mitigation measures based on a difference between the first set of values and the second set of values associated with the financial parameters; computing, by the computation engine 106, a capex stressor component index associated with the mitigation measures based on the capex stressor component (CSC) and a pre-defined base value; plugging, by the solver 104, the calculated annualized loss of each stressed configuration into the financial model 102a to determine a third set of values associated with the financial parameters; computing, by the computation engine 106, a margin stressor component (MSC) associated with the mitigation measures based on the first set of values and the third set of values associated with the financial parameters; and computing, by the computation engine 106, a margin stressor component index associated with the mitigation measures based on the margin stressor component (MSC) and the pre-defined base value.

In an embodiment, the base value is selected to be the difference between the first set of values associated with the financial parameters and a set of corresponding minimum acceptable values. In another embodiment, the base value is selected to be the difference between the first set of values associated with the financial parameters and a corresponding lowest set of second and third values associated with the financial parameters.

In an embodiment, the step 214f of analyzing, by the computation engine 106, the evaluated indices to arrive at the optimal and resilient configuration comprises: classifying, by the computation engine 106, the evaluated capex stressor component and margin stressor component indices associated with each of the stressed configurations into four classes to generate a Resilience Investment Intensity (RII) matrix; generating, by the computation engine 106, a set of recommended actions indicative of configuration modifications to be implemented to infuse resilience against the stressors based on the generated RII matrix; computing, by the computation engine 106, an increase in project investment cost for each of the configuration modifications; plugging, by the solver 104, the computed increased investment cost in the financial model 102a to facilitate computation of a fourth set of values associated with the financial parameters based on the increased investment costs; ranking, by the computation engine 106, the modified configurations based on the fourth set of values associated with the financial parameters to arrive at the optimal and resilient configuration with maximum resilience against unfavorable events (stressors) on the profitability thereof.

An example showing computation of capex stressor component index and margin stressor component index from the CSC and MSC is given below. The computation helps in normalizing the CSC and MSC to a value between 0 and 100 to arrive at the RII matrix. In the following example, IRR has been taken as the project evaluation metric for demonstration purpose.

The difference in IRR between the selected configuration or network design and a minimum IRR rate acceptable (hurdle rate) for the organization is taken as the base value. For each stressor, the CSC and MSC vectors are divided by this base value times 100 to yield an index number between 0 and 100.

Example:

Assuming that for a shortlisted optimal network design or configuration, the Minimum IRR acceptable for an organization = 12%

IRR of the shortlisted configuration or network design = 16%

For a given stressor, reduction in IRR due to capex increase (CSC) = SIRR_capex = 2%

For a given stressor, reduction in IRR due to margin decrease (MSC) = SIRR_margin = 3% Then,

Capex stressor component index in RII matrix of given stressor = dIRRcapex ^QQ dlRRfrase

2%

100 =

16% — 12% 50

Margin stressor component index in RII matrix of given stressor = d!RRmargin ^QQ dlRRfrase

3%

. 6% — 12% 100 =

1 75

Alternatively, the base value 5IRR _ase may be calculated based on a difference between the (financial parameter values) IRR of the selected configuration and say 80% of the IRR of the selected configuration or any such fraction of the unstressed configuration’s IRR depending on the owner’s resilience preference. This is usually helpful when the unstressed configurations IRR is much more than the hurdle rate and/or the reduction in IRR accounting for all resilience boosters investments is still much above the hurdle rate (minimum acceptable rate) IRR of the owner. Further, the value of maximum reduction in IRR among all the stressors for both capex and margin components respectively can also be considered as the base value for respective components especially for 5IRR _ase in cases wherein the unstressed configurations IRR is much more than the minimum hurdle rate desired. This will help in better visualization of the data and thereby will aid in decision making with RII. An exemplary RII chart wherein the margin stressor component index and component stressor index are plotted for analysis is depicted in Figure 3. The matrix data are classified into four classes based on the RII as below:

RII-1 consists of set of values whose capex stressor component index is less than or equal to 50 and margin stressor component index is greater than 50.

RII-2 are the set of values falling less than or equal to 50 on both capex stressor component and margin stressor component indices.

RII-3 are the set of values greater than 50 on both capex stressor and margin stressor vectors of RII values.

RII-4 consists of values whose capex stressor component index is greater than 50 while the corresponding margin stressor component index is less than or equal to 50.

The logic for generation of recommended actions is shown in Figure 6b and explained below.

The resilience investments classified as RII-1 are “quick- wins” or “low hanging fruit” type of investments wherein with low investment, high elasticity is infused in the configuration. These are recommended to be invested along with the project investment.

RII-2 class of investments are the low investment - low return category of resilience infusers. If the sum of margin stressor components (MSC) (0IRR_margin) of all investments in this class can reduce the optimal network’s financial parameter values (IRR) less than the hurdle rate, then all the investments in this category are recommended to be made. Otherwise, depending on the organization’s risk appetite few selected investments with high CSC and/or high MSC can be made. It is generally recommended that all investments with MSC greater than 30 in RII-2 class be made.

RII-3 class of resilience infusers is characterized by High Risk-High Returns (HR-HR). While the high MSC index makes them attractive, the corresponding high CSC also makes them capex intensive. Investment decisions of RII-3 class needs to be judiciously considered especially if the sum of few or total schemes’ SIRR_capex of this category brings down the optimal IRR to a level less than the hurdle rate.

Hence, the following methodology, third stage of the deterministic approach, can be adopted for screening and identifying the right resilience infusers in RII-3 class. The methodology examines the necessity of capex investment for each RII-3 investment that needs to be carried out at the construction phase of the finalized configuration in a tree fashion from node to leaf and recommends an action. This decision tree is called RII Investment Decision Tree (RIDT). As the investments are capex intensive, operating the configuration with risk during the initial period of the project and using the profit/cash generated in the initial period for funding the RII-3 investments during the remaining course of the project’s life is looked at alternatively.

Year-on-year Debt Service Coverage Ratio (DSCR) in the financial model being more than 2 is an important criterion to decide on whether to live with the investment risk option to generate cash. The number of such years required for the project to generate cash for a given RII-3 resilience investment is called as “gestation period”.

After the gestation period, the process unit may have to be shut-down so as to execute the RII-3 modification. The loss of margin due to the unavailability of a given process unit is also factored in RIDT. Table- 1 below exemplifies the methodology with gestation period as input based on which investments are categorized and also depicts the definition of various categories on the basis of margin loss due to the aforesaid shutdown.

Table 1 RIDT - Gestation and Shutdown Profit Loss classification

Another key input for deciding whether to invest in RII-3 modification is ease of implementation for execution of the RII-3 investment during operation of the optimal configuration. Based on the ease of implementation, categorization shown in table 2 below can be defined in RIDT :

Table 2 RIDT - Ease of Execution after Gestation classification

Based on the above-mentioned aspects, an exemplary tree diagram depicting RIDT methodology is shown in Figure 4.

In an exemplary embodiment, the actions corresponding to the output symbols (01, 02, 03, and 04) may be summarized as follows:

Table 3 RIDT - Recommended Action RII-4 class of resilience infusers are characterized by High Capex-Low Returns. Their capex may outweigh the elasticity benefit induced in the configuration and may have to be carefully evaluated. Similar to RII-3 class, if the sum of few or total schemes’ SIRR_capex of RII-4 category brings down the project IRR to a level less than the hurdle rate, then the procedure followed for RII-3 class be followed for RII-4 investment schemes, otherwise the schemes can be ignored.

During RIDT analysis, each RII class’s impact on financial parameters (e.g. IRR) are examined to arrive at the recommended action, such IRR reduction (fourth set of values of financial parameters) only with a given RII class of investments are called Resilience-IRR (R-IRR).

After incorporation of the RIIs as per the methodologies put forth above, the capex of the project may increase. RII investments induce resilience or elasticity in the configuration design to minimize the loss during stressors and do not per se increase the IRR of the optimal network design arrived after the first stage of the method (IRR_P).

The increase in project capex is incorporated in the financial model 102a of shortlisted configurations to arrive at fourth set of values of financial parameters (IRRR) and the configuration with higher IRRR is the best/optimal design with maximum resilience against unfavorable events on the profitability thereof. Such optimal and resilient configuration(s) is recommended for investment.

The present disclosure also discloses a computer-implemented system 100 for determining an optimal and resilient configuration of process units in a complex process plant. Referring to Figure 1, the system comprises a memory 102, a solver 104, and a computation engine 106.

The memory 102 is configured to store a pre-trained financial model 102a. The solver 104 comprises an interface 104a, an optimizer 104b, a first computation module 104c, a second computation module 104d, and a selection module 104e. The interface 104a is configured to facilitate modelling of a configuration optimization problem to maximize a total profit of a network of the process units subject to a pre-defined set of constraints. The constraints may comprise, but are not limited to, overall material balance constraints, feed mass balance constraints, process unit capacity constraints, process unit’s product property relation constraints, product blending mass balance constraints, internal fuel mass balance constraints, loss mass balance constraints, overall utility balance constraints, internal utility balance constraints, utility unit capacity constraints, final product blend’s property constraints, blend specification constraints, feedstock availability constraints, and product demand constraints.

The optimizer 104b is configured to receive and solve the configuration optimization problem to determine a set of profit-optimized configurations of the process units. The first computation module 104c is configured to cooperate with the optimizer 104b to determine a first profit measure associated with each of the profit-optimized configurations. The second computation module 104d is configured to cooperate with the first computation module 104c to receive the determined first profit measure, and is further configured to receive a cost of investment towards each profit-optimized configuration and plug the received data into a financial model, said second computation module 104d is configured to facilitate determination of a first set of values associated with a plurality of financial parameters for each of said profit-optimized configurations, using said financial model, based on corresponding first profit measure and cost of investment. The financial parameters can be selected from the group consisting of, but not limited to, Internal Rate of Return (IRR), Net Present Value (NPV), Payback Period (PBP), and Profitability Index (PI). The selection module 104e is configured to cooperate with the second computation module 104d to select a set of optimal configurations from the profit-optimized configurations based on the determined financial parameters. The computation engine 106 is configured to cooperate with the solver 104 to receive the selected optimal configurations and subject each of the selected optimal configurations to a configuration stress test and resilience analysis (CONSTRA) to determine at least one optimal and resilient configuration of process units, wherein the optimal and resilient configuration has a maximum resilience against unfavorable events on the profitability thereof.

In an embodiment, the computation engine 106 comprises a stress induction module 106a, an identification module 106b, a third computation module 106c, a fourth computation module 106d, an index calculation module 106e, and an analyzing module 106f. The stress induction module 106a is configured to apply a plurality of pre-determined stressors on each of the selected optimal configurations. The stressors can include, but are not limited to, stressors relative to an optimal maximized margin during configuration design due to at least one of disruptions in feedstock or product price, seasonality or cyclicity in feedstock availability or product demand, reliability of the individual process units, failure in feedstock supply or product dispatch mode, introduction of superior product specification, and introduction of new statutory norm concerning the operation. The stressors may be modeled as binary events, deterministic functions, or probability distribution functions.

The identification module 106b is configured to cooperate with the stress induction module 106a to identify a set of mitigation measures required to infuse resilience against the stressors in each of the selected optimal configurations. The third computation module 106c is configured to cooperate with the identification module 106b to estimate capital costs required for introducing each of the identified mitigation measures. The fourth computation module 106d is configured to cooperate with the first computation module 106a to receive and send the stressor and the associated configuration to the solver to facilitate calculation of an annualized loss corresponding to each stressor for each of the selected optimal configurations. The index calculation module 106e is configured to cooperate with the third and fourth computation modules (106c, 106d) to facilitate calculation of a set of indices based on the calculated annualized loss of each stressed configuration and the capital costs associated with identified mitigation measures. The analyzing module 106f is configured to cooperate with the index calculation module 106e to receive and analyze the evaluated indices to arrive at the optimal and resilient configuration. The indices are indicative of an investment required to create a hardware or a facility to infuse resilience against each of the stressors and a loss in margin corresponding to each stressor when said stressor acts on the configuration of process units.

Advantageously, the solver 104 is a mixed-integer nonlinear programming (MINLP) solver.

Advantageously, the solver 104 and the computation engine 106 are implemented using one or more processor(s). The processor may be a general-purpose processor, a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a microprocessor, a microcontroller, or a state machine. The processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. The processor may be configured to retrieve data from and/or write data to the memory. The memory may be, for example, a random-access memory (RAM), a memory buffer, a hard drive, a database, an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM), a read only memory (ROM), a flash memory, a hard disk, a floppy disk, cloud storage, and/or so forth. The memory may include a set of instructions or a control logic which the processor implements to perform the functionalities of solver 104 and the computation engine 106.

The system 100 and method 200 of the present disclosure thus introduce three stages of data driven decision-making to arrive at the best configuration or network of process units with maximum elasticity or resilience against unfavorable events on the maximized profitability of the configuration. In the first stage, the profitability of various configurations is evaluated. In the second stage, a few best configurations are selected based on profitability and the selected configurations are subjected to configuration stress test. From the stress test the mitigation options are brainstormed and their investment cost are estimated. In the third stage, analysis of the whether the mitigation investment is worth considering the cost of the stress is carried out so as to infuse the resilience against profit disruptors. The configurations are subjected to calculation of financial parameters such as IRR, NPV with capex post-resilience inducement and ranked thereafter to arrive the best configuration based on the financial parameters. Thus, the system 100 and method 200 weigh in the cost against the benefits for infusing resilience and recommend a data-driven decision against the resilience investments.

Experimental Results

The system 100 and method 200 were implemented for brownfield expansion of a 330,000 bbl/day (15 MMTPA) refinery and petrochemical complex to about 395,000 bbl/day (18 MMTPA) with addition of new network of process, petrochemical and utility units to demonstrate the applicability and working of the invention.

The following configuration options were evaluated in stage- 1: i) Bottom Upgrading Units a. VR Hydroprocessing with 75% conversion - Ebullated Bed Hydrocracking Unit (EBHCU) b. VR Hydroprocessing with 90% conversion (Slurry Bed HCU) c. Solvent De-asphalting Unit (SDA) d. Delayed Coking Unit (DCU) ii) Secondary Processing Units a. High Propylene RFCC Unit b. Full Conversion Hydrocracker Unit/Once-through Hydrocracker (OHCU) c. VGO-HDT Unit d. PFCCU e. Alkylation Unit f. Diesel and Kero treatment units. g. High PRFCC Gasoline Treatment units iii) Petchem Units a. Ethylene Oxide/ Ethylene Glycol (MEG) b. RCP-Polypropylene c. Ethyl Benzene/Styrene Monomer iv) Auxiliary Units a. New Hydrogen generation unit(HGU) b. Sulphuric acid unit c. Sulphur Recovery Unit (SRU) d. Sour water stripper (SWS) e. Amine regeneration unit (ATU) The above configurations options were evaluated in the solver 104, the corresponding cost of investment towards each configuration were determined and the profit derived from each configuration option were plugged into the financial model 102a to facilitate computation of corresponding IRR.

Three best configurations were selected based on the IRR and were subjected to CONSTRA based on which the final best configuration was arrived.

The stressors considered for analysis along with corresponding margin stressor component index and capex stressor component index are indicated below:

RII chart arrived based on the above indices is shown in Figure 5.

From Figure 5, it can be seen all the stressors fall under RII-2 investment class except the following:

HGU-3 breakdown (RAM) - HPRFCC operating at a different mode than considered in Configuration

(Miscellaneous)

Sulfur block (WSA) breakdown (RAM)

DCU breakdown (RAM)

EBF1CU breakdown (RAM) The RII matrix was analyzed to make decisions regarding resilience boosting investment(s) and subsequently to arrive at the optimal configuration with maximum resilience against unfavorable events/ stressors.

It was observed that, post application of CONSTRA, the second-best configuration had DCU instead of EBHCU as the bottom upgrader and the third best configuration had DCU instead of EBHCU along with PFCCU instead of HPRFCC. These configurations were dropped because post-CONSTRA investment, their IRR (IRR_R) dropped significantly below hurdle rate and hence were risky configurations.

After stage- 1 study, the selected configuration had a profit of 6091 INR crores (-932 Million USD) for a capital investment of 20775 INR crores (-3.2 Billion USD) with an IRR (IRRp) of 13.8%. The corporate owner’s hurdle rate IRR was 12%.

Post-CONSTRA stage-3, capex including resilience boosters increased to 22793 INR crores (-3.5 Billion USD) with an IRR (IRR_R) of 12.0%.

From the analysis of the stressors effect on the profit, mitigation measures and the corresponding resilience booster’s investment details, it was observed that CONSTRA not only provided insight on resilience investments to be made, but also at what stage of project that the investment be made. The existing methodologies do not provide this level of clarity on resilience investments and their potential impacts on the operation, even on small flexibility improvement schemes. The foregoing description of the embodiments has been provided for purposes of illustration and not intended to limit the scope of the present disclosure. Individual components of a particular embodiment are generally not limited to that particular embodiment, but, are interchangeable. Such variations are not to be regarded as a departure from the present disclosure, and all such modifications are considered to be within the scope of the present disclosure.

TECHNICAL ADVANCEMENTS

The present disclosure described herein above has several technical advantages including, but not limited to, a computer-implemented system and method for determining an optimal and resilient configuration of process units that: is cost-effective;

• determines the best configuration or network of process units with maximum elasticity or resilience against unfavorable events on the maximized profitability of the configuration;

• weighs in the cost against the benefits for infusing the resilience and recommends a data driven decision against the resilience investments;

• uses deterministic approach for evaluation of financial impact of stress on process unit configurations;

• examines the impact of stressors on the configurations design, the resilience boosters required to mitigate the same, and their corresponding investment so as to rightly decide on the investment required to boost the resilience versus its benefit;

• examines the disruption due to feedstock cost or product price on a process industry configuration; and

• determines at what stage of project that the resilience investments should be made.

The disclosure has been described with reference to the accompanying embodiments which do not limit the scope and ambit of the disclosure. The description provided is purely by way of example and illustration.

The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

The foregoing description of the specific embodiments so fully revealed the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.

Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element or group of elements, but not the exclusion of any other element or group of elements.

The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.

While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

Claims

CLAIMS:

1. A computer-implemented method (200) for determining an optimal and resilient configuration of process units in a complex process plant, said method (200) comprising:

• modelling (202), using a solver (104), a configuration optimization problem to maximize a total profit of a network of said process units subject to a pre-defined set of constraints;

• solving (204), using said solver (104), said configuration optimization problem to determine a set of profit-optimized configurations of said process units;

• evaluating (206), by said solver (104), a first profit measure associated with each of said profit-optimized configurations;

• plugging (208), by said solver (104), said determined first profit measure and a cost of investment towards each profit-optimized configuration into a financial model (102a);

• determining (210), by said financial model (102a), a first set of values associated with a plurality of financial parameters for each of said profit-optimized configurations based on corresponding first profit measure and cost of investment;

• selecting (212), by said financial model (102a), a set of optimal configurations from said profit-optimized configurations based on said financial parameters; and

• subjecting (214), by a computation engine (106), said selected optimal configurations to a configuration stress test and resilience analysis (CONSTRA) to determine at least one optimal and resilient configuration of process units, wherein said optimal and resilient configuration has a maximum resilience against unfavorable events on the profitability thereof.

2. The method (200) as claimed in claim 1, wherein said step of subjecting (214) each of said selected optimal configurations to said configuration stress test and resilience analysis comprises:

• applying (214a), by said computation engine (106), a plurality of pre determined stressors on each of said selected optimal configurations; • identifying (214b), by said computation engine (106), a set of mitigation measures required to infuse resilience against said stressors in each of said selected optimal configurations;

• estimating (214c), by said computation engine (106), capital costs required for introducing each of said identified mitigation measures;

• sending (214d), by said computation engine (106), the stressors applied on each configuration to said solver (104) to facilitate calculation of an annualized loss corresponding to each stressor for said selected optimal configurations;

• facilitating (214e), by said computation engine (106), calculation of a set of indices based on said calculated annualized loss of each stressed configuration and said capital costs associated with the identified mitigation measures; and

• analyzing (214f), by said computation engine (106), said evaluated indices to arrive at said optimal and resilient configuration of process units.

3. The method (200) as claimed in claim 2, wherein said step of sending (214d) the stressors applied on each configuration to said solver (104) to facilitate calculation of said annualized loss corresponding to each stressor for said selected optimal configurations comprises:

• determining, by said solver (104), a second profit measure for each of said selected optimal configurations for each stressor;

• determining, by said solver (104), a total loss potential for each stressor based on the difference between said first profit measure and said second profit measure; and

• multiplying, by said solver (104), a probability of stressor’s occurrence in a year’s time with said determined total loss potential to calculate said annualized loss corresponding to each stressor for said selected optimal configurations.

4. The method (200) as claimed in claim 2, wherein said step of facilitating (214e) calculation of said set of indices based on the calculated annualized loss of each stressed configuration and said capital costs associated with identified mitigation measures comprises:

• receiving, by said financial model (102a), said estimated capital costs associated with each of said mitigation measures;

• evaluating, by said financial model (102a), a second set of values associated with said financial parameters for each of said selected optimal configurations;

• receiving, by said computation engine (106), said evaluated second set of values associated with said financial parameters from said financial model (102a);

• computing, by said computation engine (106), a capex stressor component (CSC) associated with said mitigation measures based on a difference between said first set of values and said second set of values associated with said financial parameters;

• computing, by said computation engine (106), a capex stressor component index associated with said mitigation measures based on said capex stressor component (CSC) and a pre-defined base value;

• plugging, by said solver (104), said calculated annualized loss of each stressed configuration into said financial model (102a) to determine a third set of values associated with said financial parameters;

• computing, by said computation engine (106), a margin stressor component (MSC) associated with said mitigation measures based on said first set of values and said third set of values associated with said financial parameters; and

• computing, by said computation engine (106), a margin stressor component index associated with said mitigation measures based on said margin stressor component (MSC) and said pre-defined base value.

5. The method (200) as claimed in claim 2, wherein said step of analyzing (214f), by said computation engine (106), said evaluated indices to arrive at said optimal and resilient configuration comprises: • classifying, by said computation engine (106), said evaluated capex stressor component and margin stressor component indices associated with each of said stressed configurations into four classes to generate a Resilience Investment Intensity (RII) matrix;

• generating, by said computation engine (106), a set of recommended actions indicative of configuration modifications to be implemented to infuse resilience against said stressors based on said generated RII matrix;

• computing, by said computation engine (106), an increase in project investment cost for each of said configuration modifications;

• plugging, by said solver (104), said computed increased investment cost in said financial model (102a) to facilitate computation of a fourth set of values associated with said financial parameters based on said increased investment costs;

• ranking, by said computation engine (106), said modified configurations based on said fourth set of values associated with the financial parameters to arrive at said optimal and resilient configuration with maximum resilience against unfavorable events on the profitability thereof.

6. The method (200) as claimed in claim 2, wherein said stressors include stressors relative to an optimal maximized margin during configuration design due to at least one of disruptions in feedstock or product price, seasonality or cyclicity in feedstock availability or product demand, reliability of the individual process units, failure in feedstock supply or product dispatch mode, introduction of superior product specification, and introduction of new statutory norm concerning the operation.

7. The method (200) as claimed in claim 2, wherein said stressors are modeled as binary events, deterministic functions, or probability distribution functions.

8. The method (200) as claimed in claim 4, wherein said base value is a difference between said first set of values associated with the financial parameters and a set of corresponding minimum acceptable values.

9. The method (200) as claimed in claim 4, wherein said base value is a difference between said first set of values associated with the financial parameters and a corresponding lowest set of second and third values associated with the financial parameters.

10. The method (200) as claimed in claim 1, wherein said financial parameters are selected from the group consisting of Internal Rate of Return (IRR), Net Present Value (NPV), Payback Period (PBP), and Profitability Index (PI).

11. The method (200) as claimed in claim 1, wherein said solver is a mixed-integer nonlinear programming (MINLP) solver.

12. The method (200) as claimed in claim 1, wherein said constraints comprise overall material balance constraints, feed mass balance constraints, process unit capacity constraints, Process unit’s product property relation constraints, product blending mass balance constraints, internal fuel mass balance constraints, loss mass balance constraints, overall utility balance constraints, internal utility balance constraints, utility unit capacity constraints, final product blend’s property constraints, blend specification constraints, feedstock availability constraints, and product demand constraints.

13. A computer-implemented system (100) for determining an optimal and resilient configuration of process units in a complex process plant, said system (100) comprising: a. a memory (102) configured to store a pre-trained financial model (102a); b. a solver (104) comprising: i. an interface (104a) configured to facilitate modelling of a configuration optimization problem to maximize a total profit of a network of said process units subject to a pre-defined set of constraints; ii. an optimizer (104b) configured to receive and solve said configuration optimization problem to determine a set of profit-optimized configurations of said process units; iii. a first computation module (104c) configured to cooperate with said optimizer (104b) to determine a first profit measure associated with each of said profit-optimized configurations; iv. a second computation module (104d) configured to cooperate with said first computation module (104c) to receive said determined first profit measure, and further configured to receive a cost of investment towards each profit- optimized configuration and plug the received data into a financial model, said second computation module (104d) configured to facilitate determination of a first set of values associated with a plurality of financial parameters for each of said profit-optimized configurations, using said financial model, based on corresponding first profit measure and cost of investment; and v. a selection module (104e) configured to cooperate with said second computation module (104d) to select a set of optimal configurations from said profit-optimized configurations based on said determined financial parameters, and c. a computation engine (106) configured to cooperate with said solver (104) to receive said selected optimal configurations and subject each of said selected optimal configurations to a configuration stress test and resilience analysis (CONSTRA) to determine at least one optimal and resilient configuration of process units, wherein said optimal and resilient configuration has a maximum resilience against unfavorable events on the profitability thereof, wherein said solver (104) and said computation engine (106) are implemented using one or more processor(s).

14. The system (100) as claimed in claim 13, wherein said financial parameters are selected from the group consisting of Internal Rate of Return (IRR), Net Present Value (NPV), Payback Period (PBP), and Profitability Index (PI).

15. The system (100) as claimed in claim 13, wherein said solver (104) is a mixed-integer nonlinear programming (MINLP) solver.

16. The system (100) as claimed in claim 13, wherein said constraints comprise overall material balance constraints, feed mass balance constraints, process unit capacity constraints, Process unit’s product property relation constraints, product blending mass balance constraints, internal fuel mass balance constraints, loss mass balance constraints, overall utility balance constraints, internal utility balance constraints, utility unit capacity constraints, final product blend’s property constraints, blend specification constraints, feedstock availability constraints, and product demand constraints.

17. The system (100) as claimed in claim 13, wherein said computation engine (106) comprises:

• a stress induction module (106a) configured to apply a plurality of pre determined stressors on each of said selected optimal configurations;

• an identification module (106b) configured to cooperate with said stress induction module (106a) to identify a set of mitigation measures required to infuse resilience against said stressors in each of said selected optimal configurations;

• a third computation module (106c) configured to cooperate with said identification module (106b) to estimate capital costs required for introducing each of said identified mitigation measures;

• a fourth computation module (106d) configured to cooperate with said third computation module (106c) to receive and send the stressors applied on each configuration to said solver to facilitate calculation of an annualized loss corresponding to each stressor for each of said selected optimal configurations;

• an index calculation module (106e) configured to cooperate with said third and fourth computation modules (106c, 106d) to facilitate calculation of a set of indices based on said calculated annualized loss of each stressed configuration and said capital costs associated with identified mitigation measures; and

• an analyzing module (106f) configured to cooperate with said index calculation module (106e) to receive and analyze said evaluated indices to arrive at said optimal and resilient configuration.

18. The system (100) as claimed in claim 17, wherein said stressors include stressors relative to an optimal maximized margin during configuration design due to at least one of disruptions in feedstock or product price, seasonality or cyclicity in feedstock availability or product demand, reliability of the individual process units, failure in feedstock supply or product dispatch mode, introduction of superior product specification, and introduction of new statutory norm concerning the operation.

19. The system (100) as claimed in claim 17, wherein said stressors are modeled as binary events, deterministic functions, or probability distribution functions.

20. The system (100) as claimed in claim 17, wherein said indices are indicative of an investment required to create a hardware or a facility to infuse resilience against each of said stressors and a loss in margin corresponding to each stressor when said stressor acts on the configuration of process units.