Classifications

• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
  • G16C10/00—Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B17/00—Systems involving the use of models or simulators of said systems
  • G05B17/02—Systems involving the use of models or simulators of said systems electric
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
  • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
  • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
  • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
  • G05B13/044—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance not using a perturbation signal
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
  • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
  • G16C20/10—Analysis or design of chemical reactions, syntheses or processes
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
  • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
  • G16C20/70—Machine learning, data mining or chemometrics

Definitions

• the present invention relates to the field of modeling and simulation in process engineering.
• the present invention relates to a system and method for computer simulation of a chemical or biochemical process.
• the present invention falls within this framework.
• the embodiments of the invention provide an industrial tool for engineering fine chemical processes.
• a functional technical characteristic of the embodiments resides in the simulation of processes for their subsequent implementation in installations for the production of chemical or biochemical products.
• the embodiments make it possible to predict, in a concrete manner, the behavior of chemical or biochemical processes in order to make a certain number of estimates, in particular on their industrial feasibility.
• the embodiments of the invention thus make it possible to direct the development of methods chemical or biochemical with sufficient precision, and within reasonable time and cost, to make it possible to estimate the chances of success of their industrial implementation, even before the installation of an installation and the commissioning of this installation.
• the embodiments of the invention thus provide the industrial tool lacking today in the development of chemical or biochemical installations. They make it possible to determine very early on, the industrial feasibility or any difficulties in implementing a process or chemical or biochemical installations.
• a purely intellectual implementation of a simulation of chemical or biochemical processes is not possible. This is particularly the case with simulations for industrial needs. In practice, it is impossible to carry out the calculations necessary to arrive at sufficiently precise and significant results allowing industrial decisions to be taken such as, for example, the installation of chemical installations. In addition, the purely intellectual implementation of the simulation would take a prohibitive time, not particularly to fix the parameters of the model.
• An aim of the invention is to enable the fine chemicals and biotechnology industries to use relevant simulators by providing a tool that can be used by a non-expert in simulation, which allows a project to be taken into account. production with knowledge of physico-chemistry initially almost non-existent.
• This tool must have the capacity to help the user to identify the delicate or limiting points of the process in evaluation in order to determine the knowledge to be acquire or use in order to achieve a given objective, to refine these forecasts as new knowledge becomes available.
• This tool will also have to be reliable, easy to use, and allow an estimation precision of the technico-economic performances of the studied process which is adapted to the problem posed and which can evolve according to the needs and the evolution of the industrial project.
• the embodiments of the invention are based on a simulation taking into account the physicochemical characteristics of the reagents and intermediates used in the process or the simulated chemical or biochemical installation.
• the invention relates to a system according to claim 1.
• fine chemicals also called “specialty chemicals” is meant in the present document a branch of the chemical industry which synthesizes specific products, in low production volumes, but with high added value, and responding to high technical constraints, for example of purity.
• biotechnology technologies for the production of molecules by fermentation, cell cultures, extraction from the natural medium.
• algebraic equation is meant an equation having one or more real variables as an unknown.
• differentiated equation an equation having one or more functions as an unknown; it takes the form of a relation between these unknown functions and their successive derivatives.
• explicit equation is meant an equation between different variables where one variable is explained as a function of the others.
• input state variable or “output state variable” is meant a chemical variable which describes what goes in or out, respectively, of a given item of equipment, for example the temperature, the temperature. pressure, concentration ...
• predictive model is meant the association of a certain number of algebraic and differential equations based on concepts of chemistry and / or physics, the resolution of which allows the determination of the internal state variables of at least an operation performed in at least one item of equipment.
• operation is meant a transformation making it possible to go from an input state to an output state (or from an initial state to a final state).
• a chemical or biochemical process comprises a plurality of operations.
• Figure 1 illustrates a chemical process implementing several operations 01, 02, 03, 04 and several items of equipment E1, E2, E3, E4.
• Figure 2 schematically illustrates the system according to one embodiment of the invention.
• Figure 3 is a diagram illustrating steps for performing the simulation in accordance with one embodiment of the invention.
• Figure 4 shows a block diagram representing a device for implementing one or more embodiments of the invention of the invention.
• FIG. 5 represents a reaction diagram comprising a reactor with an input In and two outputs Out1, Out2.
• FIG. 6 schematically illustrates a system 600 according to embodiments
• FIG. 7 illustrates a design mode of the prior art
• Figure 8 illustrates the processing operations in a system according to the invention
• FIG. 9 is an operating diagram of the traditional method for purifying DVL
• FIG. 10 is a flow diagram of the DVL purification process with recycling of the extraction solvent
• FIG. 11 is a flow diagram of the process for purifying DVL by thermal neutralization of peroxide
• FIG. 12 is an equipment diagram of the DVL purification process with recycling of the extraction solvent
• Figure 13 is an equipment diagram of the DVL purification process by thermal neutralization of peroxide
• Figure 14 is a flow diagram of the process for producing sertraline-tetralone without racemization
• Figure 15 is a flow diagram of the sertraline-tetralone production process with racemization
• Figure 16 is a flow diagram of the sertraline-tetralone production process with racemization and recycling of the acetonitrile from racemization,
• Figure 17 is an equipment diagram of the sertraline-tetralone production process without racemization
• Figure 18 is an equipment diagram of the sertraline-tetralone production process with racemization and recycling of the racemized acetonitrile.
• a chemical process can be divided into a plurality of operations performed in a plurality of equipment.
• the number of operations can be less, greater or equal to the number of devices. Indeed, the number of operations may be greater than the number of equipment if several operations are performed. in the same equipment while the number of equipment may be greater than the number of operations if an operation requires several equipment.
• FIG. 1 schematically illustrates these different scenarios with various operations 01 to 04 and various items of equipment E1 to E4 to implement these operations.
• the operations can consist of mixtures, separations and various reactions.
• the equipment can consist of reactors, mixers, extractors, filters of evaporators etc.
• a raw material is received by the equipment E1 to carry out an operation 01.
• the product from the equipment 1 (which is not only the result of the operation 01 as seen below) , is supplied to the device E2 to perform operation 02.
• a feedback loop exists between the devices E2 and E1.
• the product from equipment E2 is supplied to both equipment E1 and equipment E3.
• An additional operation 03 is therefore also carried out in equipment E1.
• operation 01 and 03 are therefore carried out within the same equipment E1.
• the product from the E2 equipment is also supplied to the E4 equipment.
• operation 04 is carried out using two devices E3 and E4.
• E3 can be a reactor and the equipment E4 a heat exchanger).
• An operation (i) and an item of equipment (j) make it possible to go from an input state (Y lj ) to an output state (Y lj ).
• the input and output state variables are vectors representing input and output chemical quantities of an operation and equipment of a chemical or biochemical process.
• Internal state variables are vectors representing internal chemical quantities of an operation and equipment of a chemical or biochemical process.
• the algebra-differential equations thus created are assigned a very low time constant compared to the characteristic time of the operation. It should be noted that those skilled in the art know the characteristic time of each operation of the method and will know how to choose a time constant that is low with respect to this characteristic time (for example a time constant of the order of a second). By choosing a weak time constant, for a time much greater than this time constant, the differential term vanishes in steady state and it only remains to ensure that the other terms of the differential equation converge towards the conditions of the explicit algebraic equation.
• the explicit algebraic equations can be integrated into the simulation system and the user can choose between fine modeling or not of each operation. So, while moving from an algebraic system to an algebra-differential system may seem like a more complex simulation, it actually allows flexibility in the simulation without penalizing computational performance. The user can choose one or the other of the models, using the same equation solving module.
• the operator can therefore choose to describe an operation using a system of equations involving internal state variables or an explicit algebraic equation. If at least one explicit algebraic equation is selected for at least one operation of the method, the system according to the invention replaces this explicit algebraic equation into an algebra-differential equation in order to be able to integrate it into the system of equations of the whole of the process.
• Figure 2 illustrates a block diagram of a simulation module according to one embodiment of the invention.
• a client device 100 connects to the simulation system 200 according to the invention.
• the simulation module 200 according to the invention comprises a reception module 201, optionally a selection module 202, a processing module 203 and a module for solving algebra-differential equations 204.
• the client device 100 connects to the reception module 201 in order to communicate to the reception module 201 an explicit algebraic equation representing a chemical or biochemical operation and connecting a vector of input state variables representing initial chemical quantities of said operation has a vector of output state variables representing final chemical quantities of said operation.
• the reception module 201 is connected to the processing module 203 in order to receive the explicit algebraic equation representing a chemical and biochemical operation and to create an algebra-differential equation so that the steady-state solution of the The algebra-differential equation thus created converges towards said vector of output state variables according to the explicit algebraic equation.
• the processing module 203 is connected to the algebra-differential equation resolution module 204.
• the resolution module 204 solves the equation in order to obtain the vector of output state variables of the operation.
• the algebra-differential equation solving module 204 is connected to the client device 100 in order to return the vector of output state variables of the operation.
• the client device 100 communicates to the reception module 201 an explicit algebraic equation of the first operation and an algebra-differential equation of the first operation.
• the client device 100 is connected to the selection module 202 so as to select a simulation via the explicit algebraic equation or a simulation via the algebra-differential equation.
• the processing module creates an algebra-differential equation converging to said vector of output state variables according to the explicit algebraic equation or else uses the existing algebra-differential equation.
• Figure 3 illustrates the steps for performing the simulation according to embodiments.
• the client module 100 communicates with the reception module 201 in step 301.
• the client module communicates to the reception module an explicit algebraic equation representing a first chemical or biochemical operation and connecting a first vector of input state variables representing initial chemical quantities of said first operation to a first vector of output state variables representing final chemical quantities of said first operation.
• the client device can also communicate an algebra-differential equation representing said first chemical or biochemical operation and connecting a first vector of input state variables; and a first vector of internal state variables of said first operation as an unknown of the equation.
• the client device can also communicate an algebra-differential equation representing a second chemical or biochemical operation and connecting a second vector of input state variables; and a second vector internal state variables of said first operation as an unknown of the equation.
• the client module 100 communicates with the reception module in step 302. During this step 302, the client device selects a simulation mode.
• the simulation mode includes either the simulation of the chemical operation through the algebra-differential equation or through the explicit algebraic equation.
• the selection module 202 communicates to the processing module the selected simulation mode and the receiving module transmits
• the processing module performs the creation step
• the processing module injects 306 into the first algebra-differential equation the expression of the vector of output state variables of said first operation according to said first explicit algebraic equation as a vector of pseudo-state variables internal of said first operation, the steady-state solution of said algebra-differential equation thus converging towards said first vector of output state variables according to the first explicit algebraic equation.
• the processing module sets the time constant in step 307. This time constant is less than the characteristic time of the first operation. This characteristic time can be provided by the user via the client module 100 and the reception module 201 to the processing module 203.
• the processing module then transmits the algebra-differential equation thus obtained to the resolution module for the resolution of equation 309 in order to obtain the vector of output state variables of the first operation.
• the resolution module transmits this vector of output state variables from the first operation to the client device during a step 310.
• the processing module is configured to receive from the reception module the second algebra-differential equation during of a step 304 and to merge the second algebra-differential equation with the first algebra-differential equation during a step 308.
• the processing module then transmits the system of algebra-differential equations to the solving module during d 'a step 309 which will solve this system and obtain a vector of output state variables of the method comprising the two operations.
• the client device selects in step 302 a simulation mode. If the simulation via the explicit algebraic equation is selected, the processing module performs the steps of creation 305, injection 306 and fixing of the time constant 307 then the step of merging 308 with the algebraic equations - differentials from the other operations of the process. If the simulation via the algebra-differential equation is selected, the processing module directly merges this algebra-differential equation with the algebra-differential equations of the other operations of the process.
• the present invention makes it possible to combine a precise simulation using an algebra-differential equation for certain critical operations and the selection of a less precise simulation using an explicit algebraic equation.
• the solution of this explicit algebraic equation being integrated into an algebra-differential equation in order to allow the fusion of the algebra-differential equations in a single system of resolution.
• a simulation module as described above, and the corresponding method, can be implemented in a more global system and offering a complete industrial design tool allowing an entire design team to cooperate in the evaluation and development of a chemical or biochemical process and the corresponding industrial installation.
• This tool groups together various functionalities such as: data management (for example experimental data concerning chemicals, physicochemical characteristics relating to raw materials or other), the processing of these data, the definition of operational units to carry out a chemical process, the definition of basic operations entering into the chemical process, the definition of an installation for the implementation of the process with material equipment, the economic and financial evaluation.
• data management for example experimental data concerning chemicals, physicochemical characteristics relating to raw materials or other
• the processing of these data the definition of operational units to carry out a chemical process
• the definition of basic operations entering into the chemical process the definition of an installation for the implementation of the process with material equipment
• the economic and financial evaluation for example experimental data concerning chemicals, physicochemical characteristics relating to raw materials or other
• Figure 6 schematically illustrates a system 600 according to embodiments. It has a central module 601 capable of controlling the system and coordinating the execution of the various modules. The system further comprises modules 602 to 607 corresponding to the various functionalities listed above.
• each of the modules 604 to 606 relating to the definition of operational units, the definition of basic operations and the definition of a chemical installation can implement a simulation module 200 as described below. before.
• the client module 201 as described above can then be the central module 601.
• the module 602 makes it possible to store and organize experimental data in a data structure which can be exploited by all the other modules of the system.
• this module offers data relating to chemical species in which raw materials, reagents and products will be decomposed throughout the process.
• Module 603 is used to process and analyze experimental data, for example to perform statistical analyzes, identify parameters of a chemical process, etc.
• the module 607 makes it possible to carry out performance estimations according to the experimental data coming from the module 602 or from the module 603 or else from the results of the simulations carried out by the modules 604 to 606.
• the module 607 also allows to make comparisons between different performance estimates or between a performance estimate and experimental data.
• a system 600 according to the embodiments of the invention makes it possible to avoid these difficulties by integrating data management in the same tool. experiments, prediction by simulation but also the global evaluation by means of past experiments.
• This innovative approach, designated by GPX, acronym of “Guess, Predict, Experimental” allows relevant and rapid evaluations to be carried out.
• Modules 605 to 606 participate in the "G” approach which allows an overall evaluation based on a simulation from algebraic equations or past economic data.
• Modules 602 to 607 participate in the "P” approach by allowing precise simulation, using differential or algebra-diffferential equations from experimental data.
• Modules 601, 602 and 607 participate in the "X” approach by providing experimental and historical data to feed the other modules of the other approaches with relevant data.
• a system thus makes it possible, in the same data system and with coherent processing operations, to carry out all the operations necessary for the evaluation of a chemical process or of a corresponding installation: reception or definition of an evaluation to be carried out with the corresponding technical data (1), definition of a block diagram with operations and corresponding equipment (2), a first simulation on the basis of this diagram (3 ), refinement of equipment and operations (4), more precise simulation (5) and evaluation (6).
• a system thus makes it possible to share information on a chemical process evaluation project, to capitalize on the information collected during the evaluations and to communicate the result of these evaluations. All this is allowed without loss of information and with great consistency in processing.
• the module 602 makes it possible to organize the data which is shared in the system. In particular, it allows the recording of experimental scientific data concerning chemical species.
• the format in which these data is recorded is shared among all modules, allowing efficient and coordinated processing between modules.
• the modules implementing simulations 604, 605 and 606 share this data format because they access the experimental data, in particular, as described below, the decomposition of the raw materials used by a chemical process. This is also the case with module 607 which allows comparisons, in particular between simulation results and experimental data.
• a project is defined within the system by means of a "project" file and a set of files a first file describes the structure of the chemical or biochemical process, a file describes the sequence of process operations and the last represents process modeling. Then, different files are created to represent the simulations of each operational unit, base operation and installation.
• a particular characteristic of the system resides in the fact that in each file, it is possible to find the decomposition of the raw materials and of the intermediate reagents broken down according to chemical species whose characteristics are stored via the module 602.
• the write and read accesses will be different for each type of file. For example, only the central module 601 will be able to access the “write project” file. The other modules will only be able to access it in read mode for the purposes of executing their functionalities.
• the module 602 will be the only one to access the files representative of the experimental data in writing.
• the other modules will only be able to access it in read mode.
• Module 603 does not have write rights in the project description files. On the other hand, he has read access to the “project” file and to the files representing the experimental data.
• the modules capable of carrying out simulations 604, 605, 606 have access to the project files and to the files representing the experimental data. They can also write access to files representing the simulations.
• the project file can take the form of an XML (extensible markup language) type file with three parts.
• a first part represents the list of chemical species used in the project.
• Another part represents the list of raw materials with an identification and a composition with reference to the list of species.
• each raw material of the second part is broken down into the chemical species of the second part.
• a third part includes the other elements involved in the reactions such as for example catalysts, resins absorbers filters etc. with a respective identifier and some physicochemical parameters.
• the identifiers given to the chemical species, raw materials and others are valid for the project file and therefore for all the functions performed by the different modules. Thus, all the modules can have access to the same decomposition and perform calculations on this decomposition.
• each block is represented by a set of three files.
• a first file, of the XML type has two parts.
• a first part comprises a list of the flows with a respective identification and the inputs and outputs of the flows.
• a second part comprises process blocks with respective identifications, a position and lists of inputs and outputs corresponding to those of the flows of the first part.
• a second file describes the operations associated with the method.
• This second XML-type file comprises a first part with the input streams of the process (listed by their identification according to the first file) with the identification of the raw materials they carry as well as their quantity. It has a second part with the lists of process blocks and their operating temperatures.
• the third file also of XML type, comprises the modeling of the process with two parts. A first part with the input flows and the physical properties of each phase of the flow and the distribution of species in each phase. A second part with the process blocks and the list of phases for each input stream with the physical properties and the distribution of chemical species. If a chemical reaction takes place in the blocks, it is described in this part of the file.
• Each type of process description has a corresponding experimental data file.
• This file has four parts.
• the first part has a description similar to that found in the first file describing the blocks and described above.
• the second part (which is optional) comprises modeling data, similar to that of the third file describing the blocks described above.
• the third part has a description identical to that of the second file describing the blocks and described above.
• the fourth part is for storing measurement results.
• the simulation files resulting from the modules 604, 605, 606 are also of the XML type and comprise four parts.
• the first part is a copy of the "project" file.
• the second part is a copy of the first file describing the simulated block.
• the third part is a copy of the second file describing the simulated block.
• the fourth part is a copy of the third file describing the simulated block.
• An additional part may include a description of the mass and energy balances expressed in terms of fluxes for mass and blocks for energy.
• they can also include mass balances and descriptions of changes in state variables such as temperature, flow rates, etc.
• the evaluations as processed by the module 607 are described in the system by a file which groups together all the data necessary for the calculation of the costs and other performance evaluation criteria.
• this file can include flow identifiers in the reaction or the simulated installation which are consistent with those of the simulation files. he can furthermore include identifiers of reference species which are consistent with the simulation files.
• the file may include unit costs for the raw materials used by the process or the simulated installation. These costs are entered for each raw material identified in a consistent manner with the simulation file.
• an assessment corresponds to a raw material or a species, it is identified according to the identifiers of these raw material or species. If it matches a feed, it will be identified based on the feed ID.
• the system treats the files as a transfer function which takes as arguments and as outputs some of the files described above. It is possible to treat all these files in a coherent way by using as common data the species in which the raw materials are broken down. Indeed, experimental data generally deal with data relating to species. The same goes for numerical simulations. Finally, when it comes to economic and financial data, they deal more with raw materials, but which are broken down into cash.
• module 602 can take as inputs the project file as well as other user data and produce the experimental data file therefrom. Using the project file ensures that all user data is expressed in terms of species versus raw materials.
• the module 605 takes as inputs the project file as well as user data (it can also use the experimental data file or other). It renders the simulation files as output.
• the definition by the project file in terms of chemical species makes it possible to make results consistent with this representation.
• module 607 takes as input the simulation files created from the project file and outputs evaluation files.
• the first step 1. is a definition of the raw materials and the species. It comprises three sub-steps 1.1, 1.2, 1.3. It can be done using modules 601 and 602.
• the first sub-step 1.1 is a definition by the user of the raw materials entering the process.
• the second sub-step 1.2 is a decomposition of the raw materials into chemical species (eg: the raw material "azeotropic alcohol” is broken down into its constituent chemical species, namely 96% ethanol and 4% water).
• the third sub-step 1 .3 is a user input of the chemical species or biological materials produced in the process.
• the second step is a definition of the Operation Block Diagrams. It comprises five sub-steps 2.1 to 2.5. It can be performed by module 605.
• an operation block diagram consisting of operation blocks is created by a user or from a laboratory recipe.
• These BOP operation blocks represent transformations of flows or quantities regardless of the volume or time involved.
• These BOP operation blocks are not associated with equipment and do not contain the concept of productivity.
• the system takes into account the relationships between the inputs and the outputs of each of the selected BOP operation blocks.
• the user can provide these relationships by extracting a model from a first library of operation models MOP1 and possibly specifying parameters of this model, for example a ratio between an input and output rate of the block.
• These relationships can relate to various parameters such as temperatures, pressures, quantities or flows of matter, concentrations in different phases. It is at this stage a mode called “Guess” in Anglo-Saxon terminology: only intuition and user experience are used, no physico-chemical information is necessary.
• step 2.3 using the operation block diagram and the relationships between the inputs and outputs of each of the selected BOP operation blocks, the system determines overall process reviews. These overall balances may relate to the production of chemical species or biological materials produced in the process, the consumption of raw materials or energy. This determination can be carried out either continuously (flow data) or discontinuously (quantity data per batch). At this stage, the system can also determine a first estimate of process performance criteria based on the consumption of raw materials and / or energy, this criterion being able to be economic or environmental.
• step 2.4 the possibility is then offered to the user of repeating step 2.1, by selecting other operation blocks or by arranging them otherwise to obtain another diagram of operation blocks and / or repeat step 2.2 by choosing other relationships between the inputs and outputs of the selected operation blocks, then perform step 2.3 again for determining the overall balances, and continue until obtaining satisfactory overall balance sheets.
• step 2.5 the possibility is offered to the user at the end of a step 2.3 or 2.4 of replacing the relations between the inputs and the outputs of some of the BOP operation blocks by relations extracted from a second library of MOP2 operation models.
• This second library of MOP2 operation models includes more elaborate characteristics, for example taking into account thermodynamic information, or phase composition.
• the system determines the overall balances, following step 2.3, on the basis of the improved characteristics.
• the user will advantageously select the operation blocks for which it is necessary to replace the input / output relationships among the most critical operation blocks in the process.
• the system can also propose a second determination of the performance criteria based on the consumption of raw materials and / or energy.
• the third step is the definition of equipment diagrams. It has six sub-steps 3.1 to 3.6. It can be carried out by modules 604, 606.
• the operation block diagram there is a transformation of the operation block diagram into an equipment diagram representing the industrial installation to be designed.
• Devices are chosen from a library of BEQGEN generic devices or from a library of specific BEQSPEC devices to perform the operations of one or more BOP operation blocks of the operation block diagram, until all of them are transformed.
• the operation blocks of the operation block diagram are generic equipment: their characteristics can remain rather vague, even idealized: a reactor can for example be defined as a perfectly mixed adiabatic system, independently of the means to achieve this result.
• the equipment in the BEQSPEC library is specific equipment. They can then have very precise characteristics and can be associated with a given manufacturer reference.
• the approach presented also allows the direct creation of an equipment diagram without the prior creation of an operations diagram. Such a shortcut can be useful, for example, if you want to represent an already existing installation.
• step 3.2 there is a specification of the various operations carried out in each item of equipment. For each operation, one specifies in particular its nature (loading of raw material, reaction, emptying, temperature change, etc.) as well as its duration. All of these equipment specifications correspond to an operating procedure such as those used in industrial workshops. This operating procedure does not involve any modeling elements.
• Step 3.3 is a determination by the system of the operating parameters of the industrial installation represented by the equipment diagram using the libraries of models MOP1 and MEQ1 as well as of the connectivity between the equipment.
• the models in the MEQ1 library are used to describe the operations performed in the equipment as entered in step 3.2.
• the MOP1 library these are models based on a macroscopic description of the phenomena and not calling on any physico-chemical data.
• the operating parameters of the industrial installation can include productivities, yields, product quality parameters, such as purity, waste production, energy consumption.
• the system can determine a first evaluation of a performance criterion based on both the consumption of raw materials, energy, size and mode of operation of the equipment. This criterion can for example be economic or environmental.
• step 3.4 the possibility is then offered to the user of repeating step 3.1, by selecting other equipment, and / or of repeating step 3.2 by choosing other characteristics for the operations. performed in the equipment, then perform step 3.3 again for determining operating parameters, and continue until parameters are obtained that meet the set constraints.
• step 3.5 the possibility is offered to the user at the end of a step 3.3 or 3.4 of replacing the characteristics of certain operations carried out in generic or specific equipment items of the equipment diagram by characteristics extracted from a second library MEQ2 of models of operations carried out in equipment.
• the models resulting from this second library are much more sophisticated and make it possible, for example, to calculate conversions in reactors or separation performance in distillation from physicochemical data.
• the system then again determines operating parameters of the industrial plant, following step 3.3, based on the improved characteristics. At this point, the system can determine performance criteria based both on the consumption of raw materials, energy, size and mode of operation of equipment.
• step 3.6 the possibility is offered to the user at the end of a step 3.3 or 3.4 of replacing the characteristics of certain operations carried out in generic or specific equipment items of the equipment diagram by characteristics extracted from a third library MEQ3 of models of operations carried out in equipment.
• the models from this third library are detailed models and make it possible, for example, to represent mixing non-idealities or complex hydrodynamic phenomena by taking into account a particular geometric characteristic of specified equipment.
• the system then again determines operating parameters of the industrial installation, following step 3.3, on the basis of the improved characteristics. At this stage, the system can determine performance criteria based both on the consumption of raw materials, energy, size and mode of operation of the equipment.
• This substep can be carried out by module 607.
• the initial information is limited, which prevents any recourse to elaborate models (whether in terms of thermodynamics or kinetics) unless resorting to measurement campaigns. long and expensive.
• the top-down approach set out here consists in carrying out a first series of calculations on the basis of information available or accessible immediately and free of charge. Then, based on these calculations, we identify the additional information actually needed to minimize the effort of collecting this information.
• the delta-valerolactone is found in an aqueous solution which also contains cyclopentanone, hydrogen peroxide and acetic acid.
• the traditional procedure consists of: neutralizing the peroxide with sodium sulphite, neutralizing acetic acid with sodium carbonate, extracting the organic molecules using an organic solvent, evaporating the water to precipitate the salts, filter the salts, evaporate the organic solvent.
• the first step is the definition of the raw materials and the species.
• Table 1 raw materials and species injected into DVL purification processes
• Table 2 List of species involved in DVL purification processes
• the second step is the definition of operation block diagrams.
• step 2.1 for the modeling of a process is the development of a diagram of operation blocks.
• Each icon in this diagram represents an operation (that is, an action on a flow or on a quantity).
• these operations are described by the relationships between their inputs and their outputs and can be represented by very simple empirical models or by more precise thermodynamic models.
• step 2.3 the resolution of the balance equations associated with the relations between the inputs and the outputs of each block and with the connectivity of the blocks between them will allow us to know the flux (or quantities) of mass and heat at each location. of the system, and in particular at the output.
• the operation block diagrams can be read both in flow (kg / h) for continuous processes and in quantity (kg) processed per operation for batch processes.
• step 2.2 relationships between the inputs and outputs of the blocks of operations are defined.
• the models of the MOP1 library provide a very macroscopic description of the effect of each operation block on the flows or through quantities. This macroscopic description lends itself well to the use of hypotheses based on intuition and experience.
• the diagram of FIG. 9 comprises two H202_quench and AA_neutralize reactions corresponding respectively to the neutralization of H202 by Na2S03 and of AcH by Na2CO3.
• This diagram also includes three LLE, SaltsFiltration and SolvEvap separations corresponding respectively to the extraction of DVL and CP by an organic solvent (with removal of water and precipitation of salts), filtration of salts, evaporation of solvent.
• the diagram of FIG. 10 is extremely similar to that of FIG. 1 with the addition of the SolvPurif operation intended to separate the solvent to be discarded from that which can be recycled - as well as the MIX1 operation intended to mix the solvent. fresh and recycled solvent.
• the diagram of FIG. 11 simply comprises the H202_quench reaction block corresponding to the thermal neutralization of H202 and the distillation separation unit corresponding to the removal of H20 and AcH.
• the mixing operations simply consist in grouping together the masses (or mass flow rates) of each species contained in all of the incoming flows.
• a ratio is provided by the user for each species to indicate its distribution among the output streams.
• these ratios are often 0% or 100% but they can take any value between 0% and 100%.
• this system of ratios is illustrated here on situations with two outgoing flows, but it also applies to situations where these flows would be more numerous as well as to balances between phases.
• the table below includes partition ratios corresponding to the hypothesis according to which, during the chemical neutralization of AcH, all the C02 generated is evacuated without entrainment of liquid.
• Table 9 an identical assumption is made for the release of 02 in the case of thermal neutralization.
• the ratios in the table below represent a liquid-liquid extraction where the aqueous and organic phases are completely immiscible.
• the entire load of the operation is brought to 100 ° C., the water is thus evaporated (enthalpy of change of state of 2260 kJ / kg). Then all the outgoing flows are brought back to 25 ° C. in the liquid state.
• Table 9 species partition ratios between H202_quench outputs in the case of thermal neutralization
• the aim is to treat a charge of 4050 kg of reaction mixture (see composition in Table 1).
• the sulfite and carbonate flow rates are calculated so as to completely neutralize H 2 O 2 and AcH.
• the flow rate of fresh MIBK is calculated so that in LLE 4000 kg of MIBK are mixed with the load of DVL and CP.
• step 2.4 when there is a laboratory recipe - that is to say an experimental procedure possibly accompanied by elements of results - the system to which this invention relates allows the entry of the information present in the recipe. It is possible to enter all the information of the recipe without prejudging their possible later use; at the same time, no information must imperatively be provided other than the nature of each transaction.
• the computer program is able to interpret the contents to create the structure of an operation block diagram based on this content.
• a laboratory recipe can for example come from a scientific article relating to a synthesis experiment. This article then constitutes one of the first sources of information on the process.
• step 2.5 For the cost assessment of step 2.5, the balance sheets in the six preceding tables will now serve as a basis for economic calculations, they make it possible to calculate the variable part of the production cost. These costs will be reported per kilogram of DVL in the MAIN_0 stream.
• method 2 allows, thanks to the recycling of the solvent, interesting savings both in terms of fresh MIBK and of the treatment of effluents. The counterpart in terms of energy is minimal.
• Method 3 allows even greater savings because the neutralization technique employed significantly reduces the quantities of raw materials to be injected and the quantities of solvents (including water) to be separated and reprocessed.
• step 3.1 each of the operation blocks of two preserved diagrams is assigned to a device. Note that several operations can be carried out in the same equipment (for example the two neutralization reactions in the case of method 2).
• the system has a library of BEQGEN generic equipment and a specific BEQSPEC equipment library, each equipment having its characteristics.
• a first volume value (somewhat arbitrary) is given for each item of equipment. This can be changed later.
• step 3.2 it is noted that at the start of process 2 (liquid-liquid extraction with recycling of solvent), the SolventTank equipment contains 3800 kg of MIBK. Then the operating procedure applied is that exposed in Table 22. For a certain number of operations, an arbitrary duration of 0.01 h is applied because it is assumed that the duration of these steps is negligible with regard to the durations of reactions and of separations.
• step 3.3 the system determines operating parameters according to guess models (MEQ1)
• the models of the MEQ1 library are used to describe the operations taking place in the equipment (see Table 22 and Table 23). They are extremely similar in principle and structure to those of the MOP1 library.
• the transition from the operations diagram to the equipment diagram therefore does not require a large additional information as long as equipment from the BEQGEN library and operation models from the MEQ1 library are used.
• the additional information essentially consists in giving durations to the operations.
• Table 26 size, economics and cost of each piece of equipment for process 2
• Table 27 size, economics and cost of each piece of equipment for process 2
• top-down approach presented here allows us to reduce the list of species to 6 entries (including no saline species) before any collection of physicochemical data, except for molar masses and normal temperatures of boiling.
• step 3.4 the system determines the operating parameters according to a standard model (MEQ2).
• MEQ2 a standard model
• thermodynamic equilibrium laws a description of the distillation using thermodynamic equilibrium laws, which will allow a more detailed assessment of the quality of the separation.
• step 3.5 the system determines the operating parameters according to a detailed model (MEQ3).
• the general objective is to minimize the cost of producing enantiomerically pure or optically enriched sertraline-tetralone (in the R form) from a mixture containing two enantiomers (R and S) by means of chromatography such as simulated moving bed chromatography (LMS)
• the information available consists of a short series of experience reports indicating what was injected into which equipment and provides some macroscopic information on the consequences observed.
• the information contained in this document could also contain certain errors and should be treated with caution.
• Step 1 definition of raw materials and species
• the only raw materials are the racemic mixture and the fresh solvent.
• acetonitrile, methanol, sodium hydroxide and hydrochloric acid should be added.
• the raw materials used and the associated species are listed in Table 29.
• the NaCl which can be generated in the process is added.
• Figure 14 simple diagram, without recycling of the S enantiomer, and without racemization.
• Figure 15 diagram with recycling of the S enantiomer, and with racemization.
• Operation block diagrams can be read as flow (kg / h) as well as quantity (kg) processed per operation.
• step 2.2 the relationships between the inputs and outputs of the operation blocks are defined.
• FIG. 14 contains only two operation blocks: an EluMix mixing operation block and an SMBJEvap separation-evaporation operation block which makes it possible to separate the enantiomers and concentrate them.
• a racemization racemization reaction block it converts the S-enantiomer into the R-enantiomer. The transformation stops when a racemic mixture is obtained.
• the EluMix and RacMix mixers receive the input materials at 25 ° C.
• compositions and temperature of the outlets are a simple linear combination of the inlets; mixtures here are supposedly ideal liquids.
• the separation ratios describe how a compound contained in the input streams is distributed between the output streams.
• the temperature, the state, the fractionation ratios are given (possibly guessed / intuited / desired) by the user. They are either 0% or 100% in the tables below, but can take any value between 0% and 100% (see Table 33). The values are based on the experience or intuition of a chemist or by some results of US Patent 6,444,854 B1.
• the PrecipFiltr operating block converts Na + and Cl- (dissolved) into solid NaCl which is sent to SaltsOut. (see Table 30):
• the RacDry operation block is described with the parameters of Table 31. They describe a perfect racemate drying operation. For this separation, the entire load is brought to 85 ° C with evaporation of SolvOut (enthaplie of change of state of 850 kJ / kg) then the two outlets are brought back to 25 ° C in the liquid state.
• step 2.3 the overall balances are determined.
• the inlet streams are sized to obtain a flow rate of 12.5 kg / h (or 100 tonnes per year for 8,000 hours) of the R-enantiomer in the raffinate stream.
• the flow rate of the FreshEluent stream is calculated so that the S3 stream satisfies the conditions of table 3.
• the thermal power to be supplied to the process amounts to 1182 kW; the thermal power to be withdrawn amounts to 1131 kW.
• the system can now use the result of the simulation (that is to say the flows or quantities circulating in the processes given in tables 34 and 35) to determine a first evaluation of the variable production costs.
• result of the simulation that is to say the flows or quantities circulating in the processes given in tables 34 and 35.
• Table 37 contribution of each entry to the variable part of the R-enantiomer production cost. The values are expressed in € / kg of R-enantiomer in the Raff stream.
• option 2 is very likely to be unattractive from an economic point of view and in terms of waste production. In the following, we will only consider options 1 and 3.
• step 3.1 each of the operation blocks of the previous diagrams is assigned to a device.
• Table 38 provides a correspondence between the operation blocks and the equipment.
• the generated equipment diagrams are shown in Figures 9 and 10.
• the equipment used here all belongs to the BEQGEN library.
• Table 38 correspondence between operation blocks and equipment.
• the types of equipment correspond to those defined in the BEQGEN library.
• RacemateMixer and S_enantio_store equipment must therefore be able to store the production of a cycle.
• This second storage device is created out of practical necessity, it has no equivalent in the diagram of operations because the approach used has so far made it possible to disregard the constraints of continuous-discontinuous interfacing.
• the system can use this information to make a first economic estimate of the fixed costs.
• the method of calculating the cost of the equipment is exactly the same as in Example 1. With the information given in Table 40, it is therefore possible to estimate the cost of the various equipment.
• Table 40 size, economic parameters and cost of each item of equipment
• Example 2 As in Example 1, the contribution of the equipment to the cost of production is calculated by considering that the investment cost of the equipment is amortized over 64,000 hours of use. We also consider a maintenance cost equivalent to 5% of CAPEX per year (or 8,000 hours).
• the cost of labor is considered secondary (for the purpose of illustration) but could be considered.
• a quality control cost 100,000 € / year and an overhead cost which equals 25% of other fixed costs.
• the cost of the adsorbent used for LMS chromatography we consider a purchase cost of 10,000 € / kg, a service life of 16,000 hours and a mass of 1 kg per kg of R-enantiomer in Raff per day. For the case involving racemization, the process is considered to be stopped 10 hours between the 20 hour cycles.
• Table 41 total production cost (in € per kg of R-enantiomer in Raff) with breakdown of fixed costs
• option 3 Due to the presence of additional equipment, option 3 has higher fixed costs than option 1. However, by combining variable and fixed costs, option 3 seems more attractive. In the following studies, only this configuration will be considered.
• the performance of the SMB (“Simulated Moving Bed”, acronym for “Simulated Mobile Bed”) has a direct impact on costs via the consumption of eluent, the amount of adsorbent or the size of the SMB which is directly dependent on the productivity.
• the technical effect of the system according to the invention is therefore to make it possible to obtain an equipment diagram of an industrial installation, then to build and operate it. Thanks to the invention, this equipment diagram can be obtained more quickly than in traditional methods. In addition, thanks to the iteration options, with improvement of the most critical elements, the resulting equipment diagram can exhibit better operating parameters than those obtained by traditional methods.
• the method and device of the invention can be used by a laboratory chemist, who does not necessarily have the skills and experience of a process engineer.
• each module can correspond to a routine of one or more computer programs.
• the system is then implemented by an overall device 400 as illustrated in Figure 4.
• the device 400 comprises a communication bus connected to:
• a central processing unit 401 such as a microprocessor, otherwise called CPU;
• a random access memory 402 otherwise called RAM, for storing an executable code of the method of the embodiments of the invention as well as the registers adapted to record the variables and parameters necessary for the implementation of the method in accordance with the modes embodiment, the memory capacity can be increased by an optional RAM connected for example to an expansion port;
• ROM read-only memory
• Network interface 404 which is typically connected to a communication network over which digital data to be processed is transmitted or received.
• Network interface 404 may be a single network interface or be composed of a set of different network interfaces (eg, wired and wireless interfaces, or different kinds of wired or wireless interfaces). Data is written to the network interface for transmission or read from the network interface for reception under the control of the software application running in the CPU 401;
• a user interface 405 for receiving inputs from a user or for displaying information to the user;
• HD hard drive
• an input / output module 407 (otherwise called I / O) for sending / receiving data from / to devices such as a video source or a display screen.
• the executable code can be stored either in the read-only memory 403, or on the hard disk 406, or on a removable digital medium such as a disk for example.
• the executable code of the programs can be received by means of a communication network, via the network interface 404, in order to be stored on one of the storage media of the communication device 400, such as the disk. hard 406, before being executed.
• the central processing unit 401 is suitable for controlling and directing the execution of the instructions or portions of software code of the program in accordance with the embodiments of the invention, which instructions are stored on one of the data carriers. aforementioned storage. After being put into service, the CPU 401 is able to execute the instructions from the main RAM memory 402 relating to a software application, for example after these instructions have been loaded from the ROM program 403 or to the disk. dur (HD) 406. This software application, when executed by the CPU 401, causes the method steps to be implemented according to the embodiments.
• the input, first output and second output state vectors contain the numbers of moles of the different species and are respectively:
• the system (Ex1) (Ex2) can be transformed into a differential system by introducing pseudo internal state variables (X x and X 2 ) and by setting: where Q is the arbitrary time constant of homogeneous cells; when t becomes much greater than Q the differential term vanishes and the state pseudo-variables converge towards:

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• 2019
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