Design Of Multiproduct Batch Plants Research Articles

Optimization of Multiproduct Batch Plant Design for Protein Production Using Gradient Boosting Algorithms

This work deals with the problem of modeling and prediction of compressive strength of concrete structure in multiproduct batch plant design of protein production found in a chemical engineering process with uncertain demand. Modeling the strength of concrete for this process is very complex. However, it can be solved by minimizing the investment cost. Therefore, the aim of this work is to minimize the investment cost and find out the number and size of parallel equipment units in each stage. For this purpose, it is proposed to solve the problem by using gradient boosting algorithms, could be interpreted as an optimization algorithm on a suitable cost function. Which take into account, the uncertainty on the demand using gaussian process modeling. The results about number and size of equipments investment cost, production time, process time and Idle times in plant obtained by gradient boosting algorithms are the best. This methodology can help the decision makers and constitutes a very promising framework for finding a set of “good solutions”.

Modelling and Prediction of Compressive Strength of Hydraulic Concrete Structure in Multiproduct Batch Plant Design for Protein Production using Extreme Gradient Boosting Regressor with Grid Search support

This work deals with the problem of modeling and prediction of compressive strength of concrete structure in multiproduct batch plant design of protein production found in a chemical engineering process with uncertain demand. Modeling the strength of concrete for this process is very complex. However, it can be solved by minimizing the investment cost. Therefore, the aim of this work is to minimize the investment cost and find out the number and size of parallel equipment units in each stage. For this purpose, it is proposed to solve the problem by using extreme gradient boosting regressor with grid search support (XGBoost), could be interpreted as an optimization algorithm on a suitable cost function, which take into account, the uncertainty on the demand using gaussian process modeling. The results about number and size of equipment’s, investment cost, production time, process time and idle times in plant obtained by light gradient boosted trees regressor are the best. This methodology can help the decision makers and constitutes a very promising framework for finding a set of “good solutions”.

Comparison of particle swarm optimization and genetic algorithm for multiproduct batch plant design of protein production

Comparison of particle swarm optimization and genetic algorithm for multiproduct batch plant design of protein production

A matheuristic approach for the design of multiproduct batch plants with parallel production lines

Batch processes are typically used to manufacture, among other, specialty and fine chemicals. As the construction of grassroot batch plants requires major investments, models for determining the optimal design of such plants have been developed over the past decades. These models are often formulated as Mixed Integer Linear Programming (MILP) models which are solved exactly. In a previous study, we introduced the concept of parallel production lines as a design option into existing mathematical plant design models. The design problem now also aims at optimising the number of production lines, their design and the allocation of products (and production quantities) to the installed lines. However, with this extension, the complexity increases significantly. To tackle this combinatorial divergence, we formulated a matheuristic solution approach which combines an iterated local search metaheuristic with exact MILP calculations. In this paper, the hybrid solution method is described and its performance, in comparison to an exact algorithm, is illustrated for several example problems. It was found that our matheuristic obtained very good solutions in significant lower computation time. As a consequence, this technique is suitable to solve more realistic instances and enables us to expand these design models with e.g. different objectives in the future.

Comparing GPU-parallelized metaheuristics to branch-and-bound for batch plants optimization

We systematically compare two approaches with the optimal design of multiproduct batch plants that are widely used, e.g., in the chemical industry. Deterministic algorithms like branch-and-bound achieve global optimality, but often require a prohibitively high computation effort. We propose an alternative, hybrid algorithm by combining two metaheuristics: ant colony optimization (ACO) and simulated annealing (SA), in order to find near-optimal solutions in a reasonable time. We develop a parallel implementation of our hybrid approach on graphics processing units using CUDA. We experimentally compare both approaches, and we show that our hybrid metaheuristic approach $$(\hbox {ACO}+\hbox {SA})$$ yields almost-optimal solutions, while computing them significantly faster than when using branch-and-bound.

Optimization of a biotechnological multiproduct batch plant design for the manufacture of four different products: A real case scenario.

In this work a biotechnological multiproduct batch plant that manufactures four different recombinant proteins for human application is described in some detail. This batch plant design is then optimized with regards to the size of equipment using a mixed-integer linear programming (MILP) formulation recently developed by us in order to find a hypothetical new biotechnological batch plant based on the information of real known processes for the production of the four recombinant protein products. The real plant was divided for practical purposes into two sub-processes or facilities: a fermentation facility and a purification facility. Knowing the specific steps conforming the downstream processing of each product, size, and time factors were computed and used as parameters to solve the aforementioned MILP reformulation. New constraints were included to permit the selection of some equipment-such as centrifuges and membrane filters-in a discrete set of sizes. For equipment that can be built according to customer needs-such as reactors-the original formulation was retained. Computational results show the ability of this optimization methodology to deal with real data giving reliable solutions for a multi-product batch plant composed of 44 unit operations in a relatively small amount of time showing that in the case studied it is possible to save up to a 66% of the capital investment in equipment given the cost data used. Biotechnol. Bioeng. 2017;114: 1252-1263. © 2017 Wiley Periodicals, Inc.

A multiproduct batch plant design model incorporating production planning and scheduling decisions under a multiperiod scenario

In this study, we propose a multiperiod mixed-integer linear programming model, which integrates several decisions related to multistage multiproduct batch plants. In general, plant designs are solved without considering operation decisions, whereas the proposed approach considers production planning as well as scheduling decisions. The time horizon comprises several periods where deterministic variations in prices, product demand limits, costs, and the availability raw materials are considered. The plant operates using different production campaigns throughout each time period. The proposed model allows the optimal plant structure (unit sizes and its duplication in parallel at each stage) to be obtained, as well as the detailed production plan for every time period. Thus, the proposed method allows assessments of the trade-offs between the different decision levels involved by considering fluctuations throughout the time horizon.

MILP reformulations for the design of biotechnological multi-product batch plants using continuous equipment sizes and discrete host selection

In this article we present a new approach, relying on mixed-integer linear programming (MILP) formulations, for the design of multi-product batch plants with continuous sizes for processing units and host selection. The main advantage of the proposed approach is its scalability, that allows us to solve, within reasonable precision requirements, realistic instances. Furthermore, we show that many other alternatives are either numerically unstable (for the problem sizes that we are interested in), unable to solve large instances, or much slower than the proposed method. We present extensive computational experiments, which show that we are able to solve almost all tested instances, and, in average, we are ten times faster than alternative approaches. As we use a high level implementation language (AMPL) we should get further time improvements if lower level implementations are used (C, C++).Reproducibility of our results can be tested using our models and data available on-line at BPLIB.11Available at: http://www.dii.uchile.cl/~daespino/

Detailed Design of Multiproduct Batch Plants Considering Production Scheduling

Most previous approaches for the design of multiproduct batch plants have assumed the simplest scheduling policy. In order to simplify the formulation and take into account that many times demands are uncertain, they have used single product campaigns to determine the plant configuration and select the unit sizes. From the commercial point of view, this production mode is not realistic: for example, huge inventories should be kept to support this approach. However, when a stable context can be assured, the simultaneous resolution of design and a more detailed scheduling allows assessing different trade-offs. This article presents a new mixed integer linear programming (MILP) formulation assuming mixed product campaigns. Now, the composition and the sequence of the batches in the campaign must be determined as well as the assignment of batches to units when parallel units are used. Taking into account that the plant configuration is simultaneously obtained, the scheduling problem must be solved without knowing the number of available units and their sizes. Several examples are presented in order to show the performance of the proposed approach.

Multiproduct batch plants design using linear process performance models

AbstractIn this contribution, a novel linear generalized disjunctive programming (LGDP) model is developed for the design of multiproduct batch plants optimizing both process variables and the structure of the plant through the use of process performance models. These models describe unit operations using explicit expressions for the size and time factors as functions of the process variables with the highest impact. To attain a linear formulation, values of the process variables as well as unit sizes are selected from a set of meaningful discrete values provided by the designer. Regarding structural alternatives, both kinds of unit duplications in series and in parallel are considered in this approach. The inclusion of the duplication in series requires different detailed models that depend on the structure selected. Thus, in a new approach for the multiproduct batch plant design, a set of potential structural alternatives for the plant is defined. © 2010 American Institute of Chemical Engineers AIChE J, 2011

Search for Optimal Design of Multiproduct Batch Plants under Uncertain Demand using Gaussian Process Modeling Solved by Heuristics Methods

This work deals with the problem of the search for optimal design of multiproduct batch chemical plants found in a chemical engineering process with uncertain demand. The aim of this work is to minimize the investment cost and find out the number and size of parallel equipment units in each stage. For this purpose, it is proposed to solve the problem in two different ways: the first way is by using Monte Carlo Method (MC) and the second way is by Genetics Algorithms (GAs). This GAs consider an effective mixed continuous discrete coding method with a four- point crossover operator, which take into account simultaneously, the uncertainty on the demand using Gaussian process modeling with two criteria maximization the Net Present Value (NPV) and Flexibility Index (FI). The results (number and size of equipment, investment cost, production time (Hi), NPV, FI, CPU time and Idle times in plant) obtained by GAs are better than the MC. This methodology can help the decision makers and constitutes a very promising framework for finding a set of "good solutions."

Enhanced genetic algorithm-based fuzzy multiobjective strategy to multiproduct batch plant design

This paper addresses the problem of the optimal design of batch plants with imprecise demands in product amounts. The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must constitute a basic part of the design problem. Rather than resorting to a traditional probabilistic approach for modeling the imprecision on product demands, this work proposes an alternative treatment by using fuzzy concepts. The design problem is tackled by introducing a new approach based on a multiobjective genetic algorithm, combined wit the fuzzy set theory for computing the objectives as fuzzy quantities. The problem takes into account simultaneous maximization of the fuzzy net present value N P ˜ V and of two other performance criteria, i.e. the production delay/advance and a flexibility index. The delay/advance objective is computed by comparing the fuzzy production time for the products to a given fuzzy time horizon, and the flexibility index represents the additional fuzzy production that the plant would be able to produce. The multiobjective optimization provides the Pareto's front which is a set of scenarios that are helpful for guiding the decision's maker in its final choices. About the solution procedure, a genetic algorithm was implemented since it is particularly well-suited to take into account the arithmetic of fuzzy numbers. Furthermore because a genetic algorithm is working on populations of potential solutions, this type of procedure is well adapted for multiobjective optimization.

Optimal design of multiproduct batch plants considering duplication of units in series

A generalized disjunctive programming (GDP) model for the optimal design of multiproduct batch plants is presented. This general model manages the duplication of units in series to perform a given operation in the process, which is an alternative that has not been considered in previous general approaches. Unlike duplication in parallel, duplication in series is only applicable to some operations which present trade-offs between duplication and other cost-impacting elements in the batch process. In order to use a fixed time and size factor model some assumptions had to be made in the operations that allow the duplication in series. To show the effectiveness of this approach, a plant that produces multiple recombinant proteins is presented and solved.

Optimization of multi-product batch plant design under uncertainty with environmental considerations

This paper presents a framework for the design and optimization of multi-product batch processes under uncertainty with environmental considerations. The uncertainties and environmental impacts are discussed. The profit and environmental impacts are considered as bi-objectives for batch plant design. The problem, thus, is formulated as a multi-objective stochastic programming problem. It can be converted into a single-objective two-stage stochastic linear programming problem using the weighted aggregation method. To solve the two-stage stochastic programming, we introduce both Monte Carlo sampling for the entire domain of the distribution function and the feasibility cut method based on dual theory in Benders’ decomposition. The detailed algorithm for problem-solving is presented. A numerical example is presented to illustrate the proposed framework.

Design of Multiproduct Batch Plants with Units in Series Including Process Performance Models

This work deals with the simultaneous optimization of process decision variables and the structure of multiproduct batch plants considering the duplication of units in series to perform a given unit operation. Performance process models allow expressing the size and time factors of the posynomial formulation as a function of the process variables with the highest impact on costs. They are added into the design problem and handled as extra constraints. Structural alternatives for the plant were defined in order to include the duplication of units in series. Thus, the problem is formulated as an optimization problem, using mixed integer nonlinear programming to minimize the total cost of the process, subjected to design specifications. The model application is illustrated with a process for the production of oleoresins. The convenience of including the duplication in series together with process variables in process optimization is tested through the resolution of various problem cases.

An AHP-based decision-making tool for the solution of multiproduct batch plant design problem under imprecise demand

This paper addresses the problem of the optimal design of batch plants with imprecise demands in product amounts. The design of such plants necessarily involves the way that equipment may be utilized, which means that plant scheduling and production must form an integral part of the design problem. This work relies on a previous study, which proposed an alternative treatment of the imprecision (demands) by introducing fuzzy concepts, embedded in a multi-objective Genetic Algorithm (GA) that takes into account simultaneously maximization of the net present value ( NPV) and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The results showed that an additional interpretation step might be necessary to help the managers choosing among the non-dominated solutions provided by the GA. The analytic hierarchy process (AHP) is a strategy commonly used in Operations Research for the solution of this kind of multicriteria decision problems, allowing the apprehension of manager subjective judgments. The major aim of this study is thus to propose a software integrating the AHP theory for the analysis of the GA Pareto-optimal solutions, as an alternative decision-support tool for the batch plant design problem solution.

Ecodesign of Batch Processes: Optimal Design Strategies for Economic and Ecological Bioprocesses

This work deals with the multicriteria cost-environment design of multiproduct batch plants, where the design variables are the equipment item sizes as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a Genetic Algorithm (GA) with a Discrete Event Simulator (DES). To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental consideration, a Multicriteria Genetic Algorithm (MUGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design.

Optimal design of batch plants under economic and ecological considerations: Application to a biochemical batch plant

This work deals with the multicriteria cost-environment design of multiproduct batch plants, where the design variables are the equipment item sizes as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a Genetic Algorithm (GA) with a Discrete Event Simulator (DES). To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental considerations, a Multicriteria Genetic Algorithm (MUGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design.

Multiobjective optimization for multiproduct batch plant design under economic and environmental considerations

This work deals with the multicriteria cost–environment design of multiproduct batch plants, where the design variables are the size of the equipment items as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a genetic algorithm (GA) with a discrete-event simulator (DES). Another incentive to use this kind of optimization method is that, there is no easy way of calculating derivatives of the objective functions, which then discards gradient optimization methods. To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental consideration, a multiobjective genetic algorithm (MOGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design.

A Framework for Multiproduct Batch Plant Design with Environmental Consideration: Application To Protein Production

This paper presents a framework for optimal design of batch plants. It consists of a master optimization algorithm, i.e., a genetic algorithm (GA) coupled to a discrete-event simulation (DES). The innovative aspect of this work is the use of “shortcut” models included in the DES for describing the unit operations. The example of a protein production process serves as an illustration to show the effectiveness of the approach. The major interest is that the use of local models for unit operations allows the computation of an environmental index in combination with an economic indicator. The optimization framework determines the plant structure (parallel units, allocation of intermediate storage tanks), the batch plant decision variables (equipment sizes, batch sizes) and the process decision variables (e.g., final concentration at selected stages, volumetric ratio of phases at the liquid−liquid extraction, ...). The results show that a plant configuration can be easily improved, only by changing the campaign policy for instance. Optimization results for monocriterion cases (miminization of investment cost and two environmental impact criteria based on biomass produced and amount of solvent used) illustrate the efficiency of the methodology, finding a set of “good” solutions which may be interesting for the decision maker.