Fuzzy Optimization of Extractive Fermentation Processes Including Cell Recycle for Lactic Acid Production

In this study, a bioprocess optimization problem was considered for a multiple‐stage extractive fermentation, including cell recycling, to produce lactic acid. The aim of the optimization problem is to obtain the maximum overall productivity, conversion and yield simultaneously, so that the optimization problem is formulated as a multi‐objective optimization procedure. The fuzzy goal attainment method was introduced to the multi‐objective optimization problem in order to obtain a trade‐off solution. The approach was also employed to determine the optimal design for two simplified continuous fermentation processes. From the computational results, the overall productivity for the fermentation processes including cell recycling, enabled a higher dilution rate so that the overall productivity was ca. thirteen‐fold higher than that of the continuous fermentation process without cell recycling.     

Optimal Temperature and pH Control for a Batch Simultaneous Saccharification and Co-Fermentation Process

Crisp and fuzzy optimization approaches were applied to design an optimal temperature and pH control policy for a batch process of simultaneous saccharification and co-fermentation (SSCF) for ethanol production from lignocellulose, using the enzyme and recombinant strain Zymomonas mobilis ZM (pZB5). To determine an optimal temperature and pH control policy, we applied the Arrhenius relationship to each rate constant to express the temperature and pH effects in the kinetic model for both saccharification and fermentation. The goal of the optimal design was to determine the optimal temperature, pH value, initial lignocellulosic concentration, and fermentation time for maximizing the ethanol productivity under the constraints of the follow-up separation specifications. The interactive crisp and fuzzy optimization methods were applied to solve the trade-off optimization problems for obtaining a compromised design. The fuzzy goal attainment approach obtained a compromised design more flexibly than did the crisp optimization. We also compared the performances for batch and fed-batch SSCF, and used various composition proportions for the batch SSCF to determine a series of optimal designs for the fuzzy goal attainment problem. Batch SSCF was slightly more effective than fed-batch fermentation, and spruce exhibited the maximum productivity because of its higher cellulose and lower hemicellulose contents compared with those of other sources.     

Multiobjective batch process design aiming at robust performances

The most common batch design approach in practice and literature is a deterministic one. However, given the uncertainties prevailing in early stages of process design, a deterministically calculated productivity is not sufficient to select one of the large number of optional designs. Therefore, we propose a Tabu Search multiobjective optimization framework, which allows to approximate the Pareto-optimal set of designs while considering uncertain variables in the initial recipe. As a novel technique, we include performance robustness as a separate objective function within the multiobjective optimization alongside with productivity of a design, thus obtaining not only designs with high productivity or solely robust designs, but both high productivity and robust designs in one set of solutions. We examined several robustness criteria as a possible quantification of performance deviations under uncertain recipe variables. The implementation of a Tabu Search framework in combination with Monte-Carlo simulation and Latin Hypercube sampling provides a huge flexibility in the problem specification, in particular in the definition of parameter uncertainties. As a result we successfully demonstrate that metaheuristic optimization techniques are capable to approximate the Pareto-optimal set under uncertainty and are able to capture potentially antagonistic solution qualities such as high productivity and robustness by multiobjective optimization. With the help of this approach, parameters can be identified that have to be put into the focus of process research and development efforts in order to obtain high performance batch process designs.     

A fuzzy multiobjective approach for minimization of injection molding defects

This paper describes a fuzzy multiobjective optimization approach for determining the set‐points of the injection molding processing parameters to minimize the defects formed on the molded parts. The severities of the defects are represented by membership functions using the fuzzy set theory. The minimization of these membership functions, which is a multiobjective optimization problem, is transformed into a substitute problem. The preference function in the substitute problem is original and is proposed specifically for characterizing the quality requirements of the injection molding defects. The formulated optimization problem is solved with design of experiments, in which the process behavior is approximated empirically by a set of quadratic polynomials that can be easily optimized. Experimental results are presented to emphasis the workability of the proposed methodology.     

化工过程分离循环系统的多目标模糊优化研究

Separation-recycle system is an important part in chemical process, and its optimization is a multiobjective problem. In this paper the process optimization procedure is proposed. The fuzzy optimization algorithm with the concept of relative importance degree (RID) is utilized to transfer multi-objective optimization (MO-O) model into a single-objective optimization (SO-O) framework. The treatment of process condensate in synthesisa mmonia plant is taken as example to illustrate the optimization procedure, and the satisfactory result demonstrates feasibility and effectiveness of the suggested method.     

How to implement environmental considerations in chemical process design: An approach to multiobjective optimization for undergraduate students

This paper expects to give some guidelines for undergraduate students about how to implement environmental considerations in chemical process design and how the introduction of these issues could have an important effect on the results obtained in the multiobjective problem optimization when economic and environmental aspects are considered simultaneously.For this work, a representative flowsheet of many chemical and petrochemical processes has been considered. This processing scheme works with a feedstock containing some inert and with not very high conversion per pass in the reactor.Therefore, the influence of the quality of raw materials, the installation of additional stages for treating effluents, the environmental impact and the optimum conversion of the chemical process are evaluated, attending both environmental and economic criteria.Our goals were, first to show how to identify the tradeoffs of a representative chemical process when environmental and economic considerations are included simultaneously in the design and its optimization. Secondly, how feedstock quality and additional waste treatment stages affect to the results obtained in the multiobjective optimization: minimize the total cost of the installation and its environmental impact.As a result, the final objective of this paper is to teach undergraduate students how multi-objective optimization could be used to make better decisions in the design of chemical processes, taking into account both economic (minimum total cost) and environmental (minimum environmental impact) performances.For each case study proposed, a detailed optimization was performed to determine Pareto curves using the ?-constraint method to identify the tradeoffs of this multiobjective optimization and ultimately determine the best alternatives, and even their optimum operational conditions attending to both criteria.It is important to remark that this problem could be solved in Excel so that it could be a good example to undergraduate students.     

Multiobjective dynamic optimization of an industrial nylon 6 semibatch reactor using genetic algorithm

The nondominated sorting genetic algorithm (NSGA) is adapted and used to obtain multiobjective Pareto optimal solutions for three grades of nylon 6 being produced in an industrial semibatch reactor. The total reaction time and the concentration of an undesirable cyclic dimer in the product are taken as two individual objectives for minimization, while simultaneously requiring the attainment of design values of the final monomer conversion and for the number-average chain length. Substantial improvements in the operation of the nylon 6 reactor are indicated by this study. The technique used is very general in nature and can be used for multiobjective optimization of other reactors. Good mathematical models accounting for all the physicochemical aspects operative in a reactor (and which have been preferably tested on industrial data) are a prerequisite for such optimization studies. © 1998 John Wiley & Sons, Inc. J Appl Polym Sci 69: 69–87, 1998     

Multiobjective Optimization of the Industrial Naphtha Catalytic Re-forming Process

1 INTRODUCTION Petroleum refining and petrochemical industries aim at maximizing one prime product while simulta-neously minimizing another accessory product to im-prove the quality of the prime product. Unfortunately, the two requirements are often conflicting or incon-sistent. It is necessary to determine the trade-off com-promises to balance the two objectives[1,2]. As the core of aromatics complex unit, catalytic reforming is a very important process for transforming naphtha into arom…     

Optimization issues of the broke management system in papermaking

This paper presents an optimization strategy for the design and operation of a broke management system in a papermaking process. A stochastic model based on a two-state Markov process is presented for the broke system and a multiobjective and bi-level stochastic optimization model is developed featuring (i) a multiobjective operational subproblem for the optimization of the broke dosage and (ii) a multiobjective design problem formulation. An efficient optimization strategy is proposed for the operational subproblem along with a simulation based Pareto optimal solution for the design problem, and illustrated with a detailed case study.     

Teaching mathematical modeling software for multiobjective optimization in chemical engineering courses

This paper expects to give undergraduate students some guidelines about how to incorporate environmental considerations in a chemical supply chain and how the introduction of these concerns have an important effect on the results obtained in the multiobjective optimization problem where both economic and environmental aspects are considered simultaneously.To extend the economic and environmental assessment outside the chemical plant and to identify the tradeoffs associated with the reality of chemical and petrochemical industries, a simplified problem of a chemical supply chain is proposed as a case study.The inclusion of environmental concerns to this economic problem make this new case study a good example for undergraduate students interested in implementing simultaneous economic and environmental considerations in the chemical process design incorporating mathematical modeling software for solving this multiobjective problem.Thus, the final objective of this paper is to show to undergraduate students how environmental together with economic considerations could have an important impact in the logistics of a supply chain and how multiobjective optimization could be used to make better decisions in the design of chemical processes including its supply chain.To reach our purpose, the Pareto curve of the supply chain is obtained using the ?-constraint method. In addition, the tradeoffs of this multiobjective optimization have been identified and analyzed and ultimately a good decision based on the set of ‘equivalent’ optimal solutions for this chemical supply chain problem determined.     

Trade-off optimal design of a biocompatible solvent for an extractive fermentation process

In this study, crisp and flexible optimization approaches are, respectively, introduced to design an optimal biocompatible solvent for an extractive fermentation process. The optimal design problem is formulated as a mixed-integer nonlinear programming model in which performance requirements of the compounds are reflected in the objective and the constraints. In general, the requirements for the objective and constraints are not rigid; consequently, the flexible or fuzzy optimization approach is applied to soften the rigid requirement for maximization of the extraction efficiency and to consider the mass flow rate and biocompatibility of solvent as the softened inequality constraints to the solvent design problem. Having elicited the membership function for the objective function and the constraint, the optimal solvent design problem can be formulated as a flexible goal attainment problem. Mixed-integer hybrid differential evolution is applied to solve the problem in order to find a satisfactory design.     

Hydrogen and CO2 Management in the Refinery with Fuzzy Multiobjective Nonlinear Programming

An innovative approach for optimization of the hydrogen network in a refinery is presented. The optimization problem was formulated as a fuzzy‐based multiobjective nonlinear programming (FMONLP), aiming at simultaneous minimization of the total annual cost and CO₂ emission. This is achieved by defining an objective function with a weighted sum of the annual cost and CO₂ emission. The weighting factors are considered as fuzzy parameters which are described based on the experts' experiences. The applicability of the proposed approach is illustrated by optimization of an Iranian refinery hydrogen network.     

Optimal flowsheet configuration of a polymerization process with embedded molecular weight distributions

We consider the optimal reactor network synthesis of a polymerization process with detailed molecular weight distributions (MWDs). Based on an industrial high‐density polyethylene (HDPE) slurry process model including an embedded MWD, a fully connected process superstructure of continuous stirred tank reactors (CSTRs) is established through the introduction of splitters. Using this generalized superstructure as a basis, two nonlinear programming (NLP) problem formulations, which simultaneously maximize the monomer conversion and minimize the deviation between the calculated and target MWDs, are developed by applying multiobjective optimization (MO) methods. Different optimal flowsheet configurations are generated by systematically manipulating a set of continuous decision variables. Several case studies that consider different specifications on MWD are conducted to illustrate the effectiveness and efficiency of the proposed synthesis approach. Numerical results show that the optimal flowsheet configurations overcome the limitations of conventional reactor network structures and help to increase reactor productivity at the desired product quality. © 2015 American Institute of Chemical Engineers AIChE J, 62: 131–145, 2016     

A continuous process for the recovery of lactic acid by reactive distillation

Separation and purification of lactic acid from the fermentation broth are difficult because of its non‐volatile nature and the presence of other organic acids. Esterification of lactic acid with methanol followed by hydrolysis of the separated methyl lactate is an effective technique for this purpose. A continuous process for recovery is proposed and various aspects are evaluated through simulation and experiments. In the proposed route, a vapor stream containing methyl lactate is removed from a two‐phase CSTR, used for esterification, operated under boiling conditions. The ester thus separated from the aqueous solution is further hydrolyzed in a reactive distillation column. Continuous removal of the volatile component (i.e. methanol) from the reaction zone increases conversion, thus pure lactic acid is obtained from the bottom of reactive distillation column. The results obtained in the experiments are compared with the simulation results. Further simulation studies were performed to obtain the suitable operating conditions for higher conversion and concentration of lactic acid. Copyright © 2006 Society of Chemical Industry     

催化重整过程的多目标优化

In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reforming process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped kinetics reaction network and has been proved to be quite effective in terms of industrial application. The primary objectives include maximization of yield of the aromatics and minimization of the yield of heavy aromatics. Four reactor inlet temperatures, reaction pressure, and hydrogen-to-oil molar ratio are selected as the decision variables. A genetic algorithm, which is proposed by the authors and named as the neighborhood and archived genetic algorithm （NAGA）, is applied to solve this multiobjective optimization problem. The relations between each decision variable and the two objectives are also proposed and used for choosing a suitable solution from the obtained Pareto set.     

催化重整过程的多目标优化

In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reforming process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped kinetics reaction network and has been proved to be quite effective in terms of industrial application. The primary objectives include maximization of.yield of the aromatics and minimization of the yield of heavy aromatics. Four reactor inlet temperatures, reaction pressure, and hydrogen-to-oil molar ratio are selected as the decision variables. A genetic algorithm,which is proposed by the authors and named as the neighborhood and archived genetic algorithm (NAGA), is applied to solve this mulfiobjective optimization problem. The relations between each decision variable and the two objectives are also proposed and used for choosing a suitable solution from the obtained Pareto set.     

Optimal biocompatible solvent design for a two-stage extractive fermentation process with cell recycling

In this study, crisp and fuzzy multiple-goal optimization approaches are respectively introduced to design an optimal biocompatible solvent to a two-stage extractive fermentation with cell recycling for ethanol production. When designing a biocompatible solvent for the extractive fermentation process, many issues, such as extractive efficiency, conversion, amount of solvent utilized and so on, have to be considered. An interactive multiple-goal design procedure is introduced to determine a trade-off result in order to satisfy such contradicted goals. Both approaches could be iterated to solve the interactive multiple-goal design problem in order to yield a trade-off result. However, the crisp optimization design is a tedious task that requires the designer to provide various pairs of the upper bounds for the design problem to obtain the corresponding solution. The fuzzy optimization approach is able to be trade-off several goals simultaneously and to yield the overall satisfactory grade for the product/process design problem.     

多目标多级串联系统优化的模糊优选动态规划技术

将模糊优选理论与动态规划技术有机地结合起来,提出多目标多级串联系统优化的模糊优选动态规划技术。为解决化工系统中的多目标多阶段优化问题提供一条新路。最后例举一多级反应器系统最优控制应用算例。     

Strategies to improve the bioconversion of processed wood into lactic acid by simultaneous saccharification and fermentation

In the one‐step conversion of wood into lactic acid by Simultaneous Saccharification and Fermentation (SSF), inhibition effects caused by hydrolysis‐ and fermentation‐derived compounds on both enzymatic activity and fermentative ability of microorganisms appear when the operation is carried out under conditions leading to high productivities. The main effects inhibiting SSF have been assessed, and the results obtained in fed‐batch experiments allowed the definition of strategies for improving the overall bioconversion process. As cellobiose caused significant inhibition of cellulases, the supplementation of media with β‐glucosidase resulted in improved kinetics and yields. The inhibition of both enzymatic activity and microbial metabolism by lactic acid was confirmed. Intermittent removal of lactic acid by passing the fermentation media through an anion‐exchange resin column resulted in increased productivities and yields. Improved conversion of pretreated wood into lactic acid (67% conversion of cellulose into lactic acid, with maximum lactic acid concentration of 108 g dm^?3 and a productivity of 0.94 g dm^?3 h^?1) was achieved combining multiple substrate addition, supplementation with fresh nutrients and enzymes and removal of lactic acid. © 2001 Society of Chemical Industry     

Application of fuzzy relational modelling to industrial product quality control

Industrial product quality control is a typical mathematical programming and optimization problem. Unfortunately, complete and precise models are not always available for many industrial processes. A fuzzy relation modelling approach is proposed to describe approximate relationships among system variables and reconcile empirical equations. Symmetric fuzzy decision-making is transformed into a non-linear function maximization problem. The approach is applied to optimization of a wood chip refining process. It is used either to improve pulp quality within the refiner operation range, or to reduce operation costs while maintaining an acceptable pulp quality.