EP4026072A1 - Système de planification, d'entretien, de gestion et d'optimisation d'un processus de production - Google Patents

Système de planification, d'entretien, de gestion et d'optimisation d'un processus de production

Info

Publication number
EP4026072A1
EP4026072A1 EP20761290.4A EP20761290A EP4026072A1 EP 4026072 A1 EP4026072 A1 EP 4026072A1 EP 20761290 A EP20761290 A EP 20761290A EP 4026072 A1 EP4026072 A1 EP 4026072A1
Authority
EP
European Patent Office
Prior art keywords
production
attribute
settings
cost
model
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP20761290.4A
Other languages
German (de)
English (en)
Inventor
Rubin Hille
Vera COLDITZ
Ingo KNABBEN
Maike TEMMING
Tamara SPIES
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Bayer AG
Original Assignee
Bayer AG
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Bayer AG filed Critical Bayer AG
Publication of EP4026072A1 publication Critical patent/EP4026072A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06316—Sequencing of tasks or work
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning

Definitions

  • the present invention relates generally to the field of model-based planning, maintenance, management and optimization of a production plant.
  • the solution according to the invention is for optimizing the production of a chemical compound and / or a formulation thereof as a result of a production process that comprises more than one sub-process. It also relates to a solution for root cause analysis to identify influencing parameters in production.
  • chemical compound or product refers to any compound that is produced by an organic or biochemical process. It can be a small or large molecule, such as polymers, polysaccharides, polypeptides, antibodies, therapeutic proteins ...
  • a production process typically includes not only the steps that lead to the product itself, but also cleaning and formulation steps as well as plant components and plant construction, Cleaning of the production plant, disposal processes, energy and medium supply, supply routes and / or recycling steps.
  • Each element of the plant or step of the production process and / or its parameters can contribute to the optimization of the production process.
  • the demand for biopharmaceuticals has been growing steadily over the past few decades. Time-to-market, cost efficiency and flexibility in production are key issues in the development of biopharmaceutical processes these days. Continuous bioproduction and disposable technologies promise a solution to overcome these obstacles, since high space-time yields can be achieved and smaller and therefore flexible systems are used.
  • the task was therefore to provide a model-based solution that supports the development and design of a more cost-effective and more robust manufacturing process and can be used for both fed-batch and continuous processes.
  • the solution should enable a prediction of a quality attribute that is essential for the manufacturing process.
  • the solution according to the invention should be able to identify the production parameters with the greatest influence on production in relation to the product, on the production plant and its operation for a campaign, as a predictor, and to make suggestions for optimizing these influencing parameters with regard to one or more Provide quality attributes for the production process.
  • the solution according to the invention should make it possible to carry out parameter and sensitivity studies for different operating modes in order to point out the variations to changes in the most important process parameters.
  • the solution according to the invention should provide suggestions for achieving quality attribute-optimized production.
  • the information generated by the methodology should provide a deeper insight into the economic viability of various manufacturing scenarios.
  • the solution according to the invention is intended in particular to compare fed-batch and continuous Production processes, in particular biotechnological processes, also enable them to be used for other issues.
  • the production model representing at least one change or cleaning step of system components with a limited useful life and the useful life of the respective system component being defined as one of the influencing parameters of the production process (also called process settings).
  • the production model represents mathematical relationships between the process settings as input variables and simulation variables, including quality attributes of the product.
  • the attribute functions specify the mathematical relationship between the process settings or simulation variable on the one hand and a quality attribute for the production process as the output variable of the method according to the invention.
  • a sensitivity analysis of the parameters influencing the production process can be carried out in the production model.
  • Production processes within the meaning of the application are, in particular, processes for the production of a chemical compound or a chemical product.
  • Chemical compound or product refers to any compound made by an organic or biochemical process. It can be a small or large molecule, such as polymers, polysaccharides, polypeptides, or antibodies.
  • Typical sub-processes in which it is assumed that they influence the quality attributes of the production process are chemical / biochemical reactions in a (bio) reactor, cleaning steps, changing consumables, cleaning steps, with or without process interruption, precultures, cell separation, cell return, chromatography, Distillation etc., recycling steps, further process interruptions, formulation of solids such as granulation. Tableting and coating, analysis steps, disposal steps. Procurement steps can also be taken into account in the solution according to the invention.
  • disposable systems are mentioned as consumables (Single Use Systems, https://dechema.de/dechema media / Downloads / Position Papers / StatPap SingleUse 2011 English - called by-dechema-original page-124930-original site-dechema eV-p-4298 -view image-1.pdf) or more general system components that have to be changed or cleaned in the course of a production, such as B. membranes, filters, sensors, pumps, bags, etc ..
  • Sub-processes and their parameters can influence both the simulation size (also called simulation result) and the quality attribute for the production process.
  • Typical quality attributes for a production process are exemplary, without being limited to them:
  • Process settings in the sense of the registration are the characteristic parameters or properties of the production process, the sub-processes and the corresponding system components as well as the consumables. Process settings can be fixed or variable over time.
  • Typical process settings are, without being limited: - Sub-processes and their duration as well as operating resources - d. H. technical facilities, machines and
  • Equipment for the operational production process also called plant components
  • Process parameters represent special process settings. These can be primary (measured parameters) and / or secondary parameters (indirect parameters, e.g. kinetic information). Examples of such process parameters are:
  • Control parameters such as level and / or flow control schemes, cascade, feedforward and / or restriction control schemes,
  • process parameters for cleaning steps are: service life, cleaning time, amount and type of cleaning agent used, disposal of (contaminated) materials.
  • process parameters for change steps are: service life, change time, process interruption time, amount and type of equipment, disposal of (contaminated) materials and / or equipment.
  • the procurement of operating resources can also be used as process parameters such as B. Working hours are taken into account.
  • process parameters for recycling steps are: concentration of the recycled material, flow rate (continuous) or quantity (batch), recycling system
  • Examples of secondary parameters are: heat flow rate, calculated from the heat balance (using volume, flow rates and temperatures), stoichiometry of the raw materials, quality attributes from previous batches or previous time intervals for continuous campaigns.
  • the latter allow the consideration of time-delayed influences of, for example, cycle flows, residues in filter (s) and container (s) - reactor, columns, etc.
  • Secondary parameters are also preferably taken into account by the production model; Values for these secondary parameters are calculated from the primary parameters as required for the production model, and provided to the production model for calculating the simulation results. Further process settings are, for example:
  • Values, value ranges or time series data can be provided for the process settings.
  • values for the process settings are provided in the form of a table.
  • Working hours and qualifications, building area, electricity consumption and water consumption are preferably set as fixed process settings for the respective sub-processes.
  • process settings can be improved through simulations and optimization steps.
  • process settings that can be optimized are, in particular, the useful life of the system component, running time, perfusion rate, cell density, most physical parameters.
  • process settings that change over time can be optimized through simulation and optimization steps.
  • Fixed process settings can be achieved by simulating scenarios and comparing the values of the production quality attribute (s).
  • Simulation results within the meaning of the registration are in particular, without being limited to:
  • Product quality attributes such as B. stability, homogeneity, purity, specificity, viscosity, loss on drying, crystallization, particle size distribution, tablet hardness, active ingredient (API) or general release of active ingredients or rate of release of active ingredients in a formulation, etc.
  • Process streams in particular of medium, gas and / or feed.
  • Product quality attributes such as B. Stability, homogeneity, purity, specificity.
  • the simulation results can be calculated as values or progress over time.
  • the multiple simulation results are optimized against one another.
  • parameter values are typically provided in the form of a table.
  • Parameters of the attribute function are typically attribute values per unit (in particular chemicals, gases, system modules), per m2 (for areas) or per hour (working time), depending on which variable the attribute function is to describe. If costs are calculated as a production quality attribute, the parameters of the attribute functions are costs per hour, costs per unit, costs per m 2 , etc. If the CO2 emissions footprint of the production process is determined, the parameters of the attribute functions are the CO2 footprints of the respective components of the production process.
  • the selection or combination of the simulation results results from an analysis of the production process of the corresponding system as well as the process settings.
  • the corresponding process models are provided based on the required simulation results.
  • At least one process model is required which specifies or represents mathematical relationships between a simulation result, i.e. output, and the process settings as input.
  • the methodology uses process models that precisely describe the dynamic behavior of the product and metabolites in the production facility.
  • the variation of a simulation result can be determined dynamically with the help of the process model become; the variation of the production quality attribute can accordingly also be determined dynamically.
  • the method uses one or more process models or partial process models and attribute functions, where: a (partial) process model specifies or represents mathematical relationships between a simulation result and process settings, an attribute function a mathematical relationship between the process settings and / or the simulation results on the one hand and the quality attribute of the production process otherwise specified or represented. For the calculation of the attribute function, parameters of the attribute functions are also required.
  • FIG. 1 The method according to the invention is shown schematically in FIG. 1
  • the corresponding value for the quality attribute of the production process is calculated.
  • the values for the process settings and the corresponding value for the quality attribute of the production process can be optimized.
  • FIG. 1 An overview of the inventive method for a cost calculation with the inputs and outputs is shown in FIG.
  • the production model is a hybrid model that can include several empirical and / or mechanistic process models or partial process models.
  • the production model comprises one or more mechanistic models for one or more steps, e.g. B. thermodynamic and / or kinetic models.
  • mechanistic models are typically fundamental models that use basic chemical and / or physical principles such as heat and mass balance, diffusion, fluid mechanics, chemical reactions, etc.
  • a mechanistic model typically consists of differential equations describing basic principles (mechanisms) that are calibrated using historical process time series data (input data). Historical process time series data are time series of process parameter values that were collected in previous batches or time periods, as well as their respective values for the measured quality attributes of the Product.
  • Further sub-process models can be described by means of data-based models such as a neural network, a combination of neural networks, or multivariate models such as partial least squares regression (PLS). It is most preferred that the production model comprises a combination of data-based and mechanistic modeling into a hybrid model. Such hybrid models are more robust because they allow a certain amount of extrapolation, which is not the case with pure data-based models. Extrapolation means that they are able to make a reliable prediction outside of the convex hull of the data set on which they have been trained. It is obvious to the person skilled in the art that the provision of the production model includes the selection of the most suitable sub-process model for describing the production process and / or the sub-processes.
  • Input data can typically be provided by process experts, procurers and literature. This data is usually collected in a database and used for model training. Typically, this data is provided in tabular form with Microsoft Excel (MS Excel 2010®) in the database via a graphical user interface. This includes e.g. B. device unit, area, manpower, consumable unit and disposal costs. In addition, the necessary quantities for z. B. Listed employees, devices and areas necessary for the sub-processes. Values or value ranges or value sequence for the defined influencing parameters are provided; these represent the process settings. For each process / sub-process that is to be examined, this information is typically collected in a tab.
  • At least one simulation result according to the above Definition calculated according to the process settings.
  • several simulation results are calculated without being limited to them.
  • the status of the system modules and / or their system components, space-time yield and / or process flows are particularly preferably calculated.
  • FIG. 2 shows the various influencing parameters in the case of using costs as a production quality attribute.
  • the values for the variable influencing parameters are typically calculated using the production model.
  • values for the quality attributes for the production process can be calculated dynamically for different scenarios on the basis of the calculated simulation results and / or values for the process settings (together they form the influencing parameters of the attribute functions).
  • the influencing parameters of the attribute functions can be divided into different groups. An attribute function is typically developed for each group.
  • the attribute functions can for example be implemented in Matlab (Matlab R2018b).
  • production costs are predicted as a production quality attribute; in this case the attribute functions are called cost functions.
  • Costs of medium and consumables e.g. gases, chemicals, waste, water, electricity .
  • a biotechnological process is selected to illustrate the solution according to the invention. It is obvious to a person skilled in the art that the solution described can be transferred to other production processes.
  • FIG. 3 shows a schematic representation of the influencing parameters of the attribute functions and their division into fixed and variable influencing parameters.
  • the production costs can be divided into different groups.
  • the costs can be calculated using the information in the database and the relevant process data of the simulated process.
  • a cost function was developed for each group.
  • the cost functions are in Matlab (Matlab R2018b) implemented. With the help of the cost functions, the costs for different scenarios (duration, cell density, perfusion rate, etc.) can be calculated dynamically on the basis of the simulated process data.
  • the input values of the cost functions come either from the database or from the simulated process data.
  • the cost functions for all groups are shown below.
  • the Lang factor method can be used for the preliminary design [JL Novais, NJ Titchener-Hooker and M. Hoare, "Economic comparison between conventional and disposables-based technology for the production of biopharmaceuticals,” Biotechnology and Bioengineering, vol. 75, no. 2, pp. 143-153, 2001 .; G. Towler and R. Sinnott, "Capital Cost Estimating,” in Chemical Engineering Design, Elsevier, 2013, pp. 389-429.].
  • the direct cost of capital can be calculated using equation (1).
  • the purchased device costs are multiplied by the sum of the long factors l i.
  • Lang factors are multipliers for calculating the EPC in costs for pipeline construction, etc.
  • JN Novais et al. Disclose examples of such Lang factors in a bioprocess by examining a bioprocess based on disposable devices.
  • a contingency factor c is also described [JL Novais, NJ Titchener-Hooker and M. Hoare, "Economic comparison between conventional and disposables-based technology for the production of biopharmaceuticals," Biotechnology and Bioengineering, vol. 75, no. 2, pp. 143-153, 2001].
  • the equipment acquisition cost typically includes all costs for reusable production equipment, e.g. B. fermenter housing, bag holder, filter housing.
  • BC building cost [ €]
  • a s, j area of process Step S and area that j [m 2 ]
  • TICj total installed costs [ € / m 2 ]
  • the required space can be determined for each modality.
  • the area for each process step (S) can either be assumed or calculated by adding up individual pieces of equipment.
  • the process steps of media preparation, reactor preparation, preculture, main culture and harvest / shutdown are preferably taken into account.
  • the direct investment cost is typically amortized over the years of the asset's service life. It is therefore converted into an annual capital fee to be paid each year for the life of the facility. This is done through an annual equity ratio (ACCR).
  • the investment costs in a year are calculated by multiplying the ACCR by the direct fixed investment (see equation (3)).
  • the operating costs are preferably described in an attribute function.
  • the fixed operating costs are preferably composed of maintenance and labor costs.
  • the variable operating costs can be divided into the following groups: consumables, media as well as materials and resources. The groups and their calculation methods are explained in more detail below outlined.
  • the functionality of a production plant should be maintained during its service life. Therefore, parts and equipment are repaired and replaced.
  • the costs incurred are usually estimated as a fraction (p) of the investment costs and are between 3% and 5% [G. Towler and R. Sinnott, "Estimating Revenues and Production Costs," in Chemical Engineering Design, Elsevier, 2013, pp. 355-387.].
  • the maintenance cost (MAC) can be calculated using equation (4). For example, the proportion (p) was assumed to be 5% for all modalities.
  • Labor costs are preferably defined as fixed operating costs as they are independent of product production [D. Petrides, "BioProcess Design and Economics,” in Bioseparations Science and Engineering, Roger G. Harrison, 2015.]. Labor costs preferably take into account all expenses (salary and benefits) for employees who work in connection with the cell culture process.
  • work planning of the processes is typically carried out.
  • employees from different groups are usually involved in terms of their functions, which are operators, process engineers, etc.
  • the number (a) of required full-time employees (FTE) of a specific group (g) can be determined [I. Knappen, M. Temming and J.
  • Single-use items ie consumables, preferably include all single-use items, e.g. B. Filters, Bags, and Quality Control Assays.
  • all consumables required for the process are listed in a database.
  • the consumption costs per batch can consist of a fixed and a variable part.
  • the fixed part takes into account the fixed costs for consumables for a batch, e.g. B. the reactor bag.
  • the variable part takes into account the costs for consumables, which are dependent on the operating parameters, such as B. the duration of the main culture, the perfusion rate and the membrane change frequency (in perfusion modality using ATF) vary.
  • CC consumable cost [ €]
  • a s, j fixed amount of consumable unit j in process Step S [-]
  • C j cost of consumable unit j [ €]
  • v sj variable amount of i of consumable unit j in process Step S [-]
  • Cells need substrate and other components to produce biomass and product. Substrate and other components are provided by medium.
  • the basal medium is used in the preculture and as the starting volume in the production bioreactor. Feed medium is continuously added to the production bioreactor during the main culture.
  • the corresponding cost function therefore preferably comprises a fixed and a variable component.
  • the cost function for medium costs for a biotechnological production process is given in equation (7), for example.
  • a certain flow of conveying medium (fFM) is supplied. In the perfusion process, this medium flow depends on the perfusion rate. It should be emphasized that the feeding medium for batch and perfusion cultures is different.
  • MC medium cost [ €]
  • a BM fixed amount of basal medium from preculture and initial start volume [L]
  • C BM cost of basal medium [ € / L]
  • C FM cost of feed medium [ € / L]
  • f FM flow of feed medium [L / h]
  • Glucose is usually required as a growth substrate in bioprocesses [NP Shirsat, NJ English, B. Glennon and M. Al-Rubeai, "Modeling of Mammalian Cell Cultures,” in Animal Cell Culture, Springer International Publishing, 2015, pp. 259-325. ].
  • the glucose concentration in the feed medium is usually insufficient, so additional glucose is added.
  • Base and acid are required to maintain the desired pH value in the bioreactor. Foam formed in the bioreactor due to gasification. Antifoam agents are used to prevent excessive foaming.
  • the material costs for the duration of the main culture result from equation (9).
  • the individual process streams f j are determined with the aid of the production model.
  • the process settings can be used, for example, to specify how much of the reactor volume is replaced with new medium per day.
  • the time series of the calculated for individual streams For example, there is a purge current that is necessary to keep the cell density constant. This is calculated on the basis of the production model.
  • Gases provide important nutrients. Normally, oxygen, nitrogen and air are introduced into the bioreactor with a suitable gassing strategy. N2 is usually only used in the start-up phase of the bioreactor in order to e.g. B. To calibrate sensors. Consumption is economical and is therefore not taken into account.
  • Waste is created in a production process. In particular when using disposable items, large amounts of solid waste arise; In this case, this sub-process gains relevance for the production quality attribute. Contaminated waste is a major problem in bioprocesses. It is therefore important to ensure that biological residues are inactivated.
  • the cost of solid and liquid (contaminated / uncontaminated) waste can be calculated by adding the total amount (weight / volume) and multiplying it by a cost factor (see equation (11)).
  • the waste for a batch can be either fixed or variable, depending on the type.
  • HVAC heating, ventilation and air conditioning
  • a batch of the production process is simulated.
  • values, value ranges or value sequence for the process settings are provided.
  • Membrane fouling depends on the flow through the membrane (“filter flux” in L / m 2 / d).
  • a basic operating mode (basic scenario) can be defined for each process modality.
  • the basic scenarios can be simulated and evaluated on an economic basis.
  • the method can also be used as a basis for optimizing the operating mode of the perfusion process using ATF with regard to economic parameters. Therefore, an optimization function was designed and solved using a genetic optimization algorithm provided by Matlab (Matlab R2018b).
  • Matlab Matlab R2018b
  • the ATF filter modules are a high cost factor in the perfusion process with ATF. They need to be changed during the process as they will block over time. If they block, fewer mAb will be sieved through the filter membrane into the crop. Both the costs of filter membranes and the amount of mAb in the harvest influence the specific costs of the goods sold (sCOGS) [1]. By minimizing the sCOGS, the optimal number and times of the filter membrane change can be determined; these represent one of the process settings that can be optimized.
  • COGS cost of goods sold, i.e. direct costs related to the production of goods sold in a company.
  • the space-time yield directly influences the sCOGS ( € / g).
  • the viable cell density and the specific productivity of a cell mainly affect the amount of mAb produced and thus the space-time yield.
  • the method according to the invention takes into account
  • Risk factors in the production process Contamination, bag leaks or production downtime are risk factors that lead to a delay in the schedule and fewer batches per year and should therefore be taken into account.
  • success rates / failure were implemented for the process to cover these errors.
  • the success rates are typically determined from the process knowledge of experts.
  • Success rate (s) are typically considered as a parameter of the attribute function.
  • a scale effect can be taken into account. For example, the cost of capital per unit of product increases with the size of the
  • cost cost of plant [ €]
  • s size of plant [i.e. kg, L]
  • n exponent [-]
  • the process scale is usually set in the process settings. With the help of the method, the calculation can also be carried out and compared for different scales and thus the scale effect can be examined.
  • the results of the cost calculation are typically cost reports as well as parameter and sensitivity studies.
  • the aim of a perfusion process is to achieve the highest possible concentration of the antibody in the harvest in order to then purify it in the subsequent downstream process. This in turn means that - due to the fouling - the filter membrane has to be exchanged for a new, fresh module after a certain period of time so that the antibody can again pass through the membrane unhindered, which in turn increases the product concentration in the harvest.
  • FIG. 4 shows a diagram of a biotechnological perfusion process with cell retention. Inflowing and outflowing media flows are shown with F in and F out, depending on the perfusion rate. H denotes the harvest, P the purge stream.
  • the optimal times should be determined at which the filter membrane is replaced so that as much antibody as possible is in the harvest at all times.
  • the membrane should be replaced as rarely as possible during the running time, since an ATF filter module makes a significant contribution to the overall costs of the process ( ⁇ 9%).
  • Another question is the total duration of a perfusion process. It was observed that the cell viability and thus also the specific productivity decrease after a certain cultivation time. It should therefore be determined when the point in time is reached at which the process no longer makes economic sense - more precisely, when the specific costs for the antibody (specific cost of goods) reach a certain threshold value.
  • a perfusion process can be described with the help of a cell and process model.
  • the model is based on a combination of a metabolic model with differential equations, the parameters of which are in turn calibrated on the basis of experimental data.
  • This approach to model development are already known [US Patent No. 10296708; Hebing, L., Neymann, T., Thüte, T., Jockwer, A., and Engeil, S. (2016). Efficient generation of models of fed-batch fermentations for process design and control. DYCOPS, 621-626]
  • the model was expanded to include the perfusion modes cell retention with an ATF module and cell retention with a settier.
  • LMD is the filter flux (flow through the membrane per m2 membrane surface in L / m2 / d).
  • Figure 5 on the left shows the product sieving coefficient over the cultivation time (time) for different flow rates.
  • Figure 5 on the right shows the fouling rate over various membrane flows (filter flux).
  • the process model has been expanded to include a further functionality for the dynamic calculation of manufacturing costs (Cost of Goods Sold, COGS).
  • COGS Cost of Goods Sold
  • a major cost factor in perfusion processes with an ATF module is, for example, the number of membrane changes.
  • the ATF filter membrane must be changed during the process, as it clogs over time and thus prevents the antibody from flowing into the harvest.
  • the ATF filter membranes make a significant contribution to the overall manufacturing costs, with the aim of keeping the number of filter changes at a low level.
  • the methodology is designed in such a way that both the model and the associated cost functions can be expanded by any parameters.
  • a function has been added to the model that describes the failure probability of the process depending on the cultivation time, influenced by risk factors such as contamination or the lifetime of the single-use equipment. With the help of this function, the entire process runtime could be optimized and the risk of the process minimized.
  • Another possible application for process optimization is the calculation of the optimal cultivation time of the cells, since the viability and consequently the productivity decrease over time and the economic efficiency of the process decreases with increasing cultivation time.
  • Figure 6 shows process data optimized with regard to the operating costs by calculating the optimal times of the ATF filter membrane change with the aid of the solution according to the invention.
  • Curve (a) shows the course of the Viable Cell Density (VCD) in the bioreactor.
  • Curve (b) shows the course of the concentration of the antibody (mAb) in the bioreactor.
  • Curve (d) shows the space-time yield.
  • Curve (e) shows the accumulated space-time yield and curve (f) shows the accumulated product. All curves were calculated for a fed-batch process (FB, blue), perfusion process with ATF model (red) and perfusion process with Settier (yellow).
  • the presented methodology was described for basic scenarios for fed-batch perfusion with alternating tangential flow filtration and inclined settling.
  • the comparison of the basic scenarios showed that perfusion modalities can cover the need for high product quantities, but have a higher sCOGS compared to the FB strategy.
  • Sensitivity studies revealed cell-related parameters, perfusion rate and mean costs as the main cost drivers for perfusion modalities.
  • Parameter studies showed that it is possible to even undercut sCOGS of the FB base scenario. In addition, they showed that increasing the space-time yield and decreasing the perfusion rate have the greatest influence on the cost savings.
  • the cell-specific productivity has a greater influence on sCOGS than the viable cell density. This can be achieved on the one hand by concentrating on the selection of highly productive clones at an early stage and on the other hand optimizing the performance of the bioreactor in order to increase the oxygen transfer and thus the viable cell density. Assuming that the space-time yield is not influenced by the reduction in the perfusion rate, a perfusion rate of 0.5 L / L / d would be sufficient only using the Settler modality to undercut sCOGS of the FB base scenario.

Abstract

La présente invention concerne d'une manière générale le domaine de la planification basée sur un modèle, de l'entretien, de la gestion et de l'optimisation d'un processus de production dans une installation de production comprenant de multiples composants d'installation avec une période d'utilisation limitée, le processus de production étant constitué de plusieurs processus partiels et comprenant au moins une étape de remplacement ou de nettoyage pour les composants d'installation avec une durée d'utilisation limitée. La solution selon l'invention est en particulier destinée à optimiser la production d'un composé chimique et/ou d'une formulation de celui-ci résultant d'un processus de production qui comprend plus d'un processus partiel. L'invention concerne également une solution en vue d'amener à une analyse afin d'identifier des paramètres d'influence de la production.
EP20761290.4A 2019-09-06 2020-08-31 Système de planification, d'entretien, de gestion et d'optimisation d'un processus de production Pending EP4026072A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP19195965 2019-09-06
PCT/EP2020/074208 WO2021043712A1 (fr) 2019-09-06 2020-08-31 Système de planification, d'entretien, de gestion et d'optimisation d'un processus de production

Publications (1)

Publication Number Publication Date
EP4026072A1 true EP4026072A1 (fr) 2022-07-13

Family

ID=67928588

Family Applications (1)

Application Number Title Priority Date Filing Date
EP20761290.4A Pending EP4026072A1 (fr) 2019-09-06 2020-08-31 Système de planification, d'entretien, de gestion et d'optimisation d'un processus de production

Country Status (4)

Country Link
US (1) US20220327457A1 (fr)
EP (1) EP4026072A1 (fr)
CN (1) CN114391153A (fr)
WO (1) WO2021043712A1 (fr)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10871753B2 (en) * 2016-07-27 2020-12-22 Accenture Global Solutions Limited Feedback loop driven end-to-end state control of complex data-analytic systems
DE102021128718A1 (de) 2021-11-04 2023-05-04 Volkswagen Aktiengesellschaft Verfahren zur Ermittlung von Prozessparametern für einen Herstellungsprozess eines realen Produkts
DE102022113686A1 (de) 2022-05-31 2023-11-30 Dr. Ing. H.C. F. Porsche Aktiengesellschaft Verfahren zur Berechnung von Nachhaltigkeitskennwerten von Fahrzeugen, Entscheidungsunterstützungssystem und dessen Verwendung
CN116629552A (zh) * 2023-05-26 2023-08-22 讯猫软件集团有限公司 一种智能工业管理调控系统

Family Cites Families (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6278899B1 (en) * 1996-05-06 2001-08-21 Pavilion Technologies, Inc. Method for on-line optimization of a plant
AU6283699A (en) * 1998-10-02 2000-04-26 Bios Group Lp A system and method for determining production plans and for predicting innovation
DE10219322A1 (de) * 2002-04-30 2003-11-20 Siemens Ag Verfahren und Vorrichtung zur Planung eines industriellen Prozesses
US20030220828A1 (en) * 2002-05-23 2003-11-27 Chih-An Hwang Polymer production scheduling using transition models
US20050027577A1 (en) * 2003-07-30 2005-02-03 Saeed Baruch I. Architecture for general purpose business planning optimization system and methods therefor
US8880202B2 (en) * 2010-07-09 2014-11-04 Emerson Process Management Power & Water Solutions, Inc. Optimization system using an iteratively coupled expert engine
US8972067B2 (en) * 2011-05-11 2015-03-03 General Electric Company System and method for optimizing plant operations
US8645076B2 (en) * 2011-06-03 2014-02-04 Rockwell Automation Technologies, Inc. Microbial monitoring and prediction
EP3051449A1 (fr) 2015-01-29 2016-08-03 Bayer Technology Services GmbH Procédé mis en oeuvre par ordinateur destiné à la fabrication d'un modèle de fermentation
EP3391252A4 (fr) * 2015-12-19 2019-06-26 Prevedere, Inc. Systèmes et procédés de prévision d'après des données de série chronologique
US20170204446A1 (en) * 2016-01-15 2017-07-20 Artemis BioSystems Inc. System for rapid continuous manufacturing of monoclonal antibodies
US11365382B2 (en) * 2016-03-14 2022-06-21 Omnibrx Biotechnologies Private Limited Bioreactor system and method thereof
WO2018035718A1 (fr) * 2016-08-23 2018-03-01 Accenture Global Solutions Limited Prédiction de production d'établissement industriel en temps réel, et optimisation de fonctionnement
EP3385366A1 (fr) * 2017-04-04 2018-10-10 Siemens Aktiengesellschaft Procédé de contrôle d'un processus biotechnologique
US11754991B2 (en) * 2018-10-25 2023-09-12 SMP Logic Systems, LLC Cloud-controlled manufacturing execution system (CLO-cMES) for use in pharmaceutical manufacturing process control, methods, and systems thereof
GB201810772D0 (en) * 2018-06-29 2018-08-15 Ge Healthcare Bio Sciences Ab Method in bioprocess purification system

Also Published As

Publication number Publication date
US20220327457A1 (en) 2022-10-13
CN114391153A (zh) 2022-04-22
WO2021043712A1 (fr) 2021-03-11

Similar Documents

Publication Publication Date Title
WO2021043712A1 (fr) Système de planification, d'entretien, de gestion et d'optimisation d'un processus de production
Chan et al. Combining lean tools application in kaizen: a field study on the printing industry
Manzoni et al. Soil carbon and nitrogen mineralization: Theory and models across scales
Revollar et al. Wastewater treatment plant operation: simple control schemes with a holistic perspective
EP3732485A1 (fr) Prédiction de l'etat métabolique d'une culture cellulaire
CN105027010A (zh) 控制参数调整方法及控制参数调整系统
Yang et al. The optimization of total laboratory automation by simulation of a pull-strategy
Meneses et al. Joint environmental and economical analysis of wastewater treatment plants control strategies: a benchmark scenario analysis
De Prada et al. Integration of RTO and MPC in the Hydrogen Network of a Petrol Refinery
Yang et al. Agricultural waste recycling optimization of family farms based on environmental management accounting in rural china
CN114223034A (zh) 用于确定细胞培养过程中的过程变量的方法
Dietz et al. A framework for multiproduct batch plant design with environmental consideration: Application to protein production
EP3251039A1 (fr) Procédé implémenté sur ordinateur pour établir un modèle de fermentation
Velayudhan Overview of integrated models for bioprocess engineering
Hoose et al. Selection of production mix in the agricultural machinery industry considering sustainability in decision making
Luca et al. Data driven detection of different dissolved oxygen sensor faults for improving operation of the WWTP control system
Wang et al. Characterization of oxidation-reduction potential variations in biological wastewater treatment processes: A study from mechanism to application
Lozano Avilés et al. Methodology for energy optimization in wastewater treatment plants. Phase I: Control of the best operating conditions
Lozano Avilés et al. Methodology for energy optimization in wastewater treatment plants. Phase III: Implementation of an integral control system for the aeration stage in the biological process of activated sludge and the membrane biological reactor
Anderson et al. Use of hybrid models in wastewater systems
DE112006003884T5 (de) Verfahren zur Verbesserung von Organismen unter Verwendung der Profilerstellung von Metaboliten-Gesamtflüssen
Revollar et al. Quantifying the benefit of a dynamic performance assessment of WWTP
Abduraupov et al. Modeling of water management for cotton production in Uzbekistan
Singh et al. An optimal decision support system based on crop dynamic model for N-fertilizer treatment
Bick et al. Boron removal from seawater reverse osmosis permeate: A Hasse diagram analysis of current technologies

Legal Events

Date Code Title Description
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: UNKNOWN

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20220406

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

DAV Request for validation of the european patent (deleted)
DAX Request for extension of the european patent (deleted)