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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 strainZymomonas mobilisZM (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. [ABSTRACT FROM AUTHOR]

Highlights: [•] A cell recycling two-tank system was used for continuous production of bioethanol. [•] Each tank fermentor equipped with a settler for recycling of flocculent yeast. [•] The system was effective for ethanol production at high dilution rates. [•] The productivity was tremendously enhanced compared with batch fermentation. [ABSTRACT FROM AUTHOR]

Abstract: Computer-aided process/solvent design is introduced to find a feasible biocompatible solvent for an extractive fermentation and separation process. The designed biocompatible solvent serves as both the extractant for extractive fermentation and the entrainer for extractive distillation, to yield water-free ethanol. Several goals, such as maximizing production rate and extraction efficiency, and limiting solvent utilization, are simultaneously considered in the optimal solvent design problem. Thus, the design was formulated as a mixed-integer nonlinear optimization problem. A two-phase computational scheme was introduced to solve the problem. The mixed-integer hybrid differential evolution (MIHDE) algorithm was first applied to solve the problem in order to obtain a feasible solution. The feasible solution was then served as an initial starting point for the mixed-integer sequential quadratic programming (MISQP) solver to numerically confirm that the optimal design was achieved. We have compared the crisp and fuzzy approaches to the design problem. The fuzzy goal attainment approach is able to address goal trade-offs and yield an overall satisfactory grade for the problem. [ABSTRACT FROM AUTHOR]

Abstract: In this study, we considered a multi-stage integrated extractive fermentation with cell recycling for ethanol production using the genetically engineered Sacchromyces yeast 1400 (pLNH33), which can utilize glucose and xylose as carbon sources to produce ethanol. Each stage consists of a stirred-tank bioreactor, a cell settler and an extractor. A generalized mathematical model was formulated to express the multi-stage integrated process. The aim of the optimization problem was to obtain the maximum overall productivity and conversions subject to the interval inequality constraints for the residual glucose and xylose concentrations and the total sugar supply. A fuzzy goal attainment method was applied to the multiobjective problem in order to achieve the maximum satisfaction for all design requirements. From the computational results, the integrated extractive fermentation with cell recycling (involving the extraction of ethanol from the extractor in situ to alleviate product inhibition) led to an optimal overall productivity that was 8.0% higher than that obtained by the method of continuous fermentation with cell recycling, and about 13-fold higher than that obtained by the method of continuous fermentation without cell recycling. [Copyright &y& Elsevier]

Abstract: In this study, the computer-aided process/solvent design is introduced to find an optimal biocompatible solvent and to maximize the ethanol production rate simultaneously for the single- or double-stage extractive fermentation process with cell recycling. Such a process/solvent design problem is formulated as a mixed-integer nonlinear programming problem that is solved by mixed-integer hybrid differential evolution in order to obtain a global design. The double-stage process can use a smaller amount of fresh solvent to increase ethanol productivity compared with that of the single-stage process, but it will also decrease overall conversion. Comparing the case studies, the simultaneous process/solvent design could yield higher overall ethanol productivity than that of the process design. The maximum ethanol production rate for the double-stage extractive fermentation with cell recycling was about 10-fold higher than that of continuous fermentation and about twofold higher than that of continuous fermentation with cell recycling. [Copyright &y& Elsevier]

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. [Copyright &y& Elsevier]

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. [Copyright &y& Elsevier]

Abstract: We analyze the growth dynamics and production of an industrially interesting amino acid in a recombinant Escherichia coli strain, using in parallel two alternative modeling frameworks, namely S-systems and lin-log models. These models have identical steady-state solutions, but differ in their representation of transients. The models were parameterized with data from two bioprocesses that differed in their initial culture concentrations and then used to predict responses under yet a different initial culture regimen. Among the S-system models, several alternatives representing slightly different pathway structures, were tested. All were found to be capable of capturing the dynamics of all variables in the test systems and also of predicting the dynamic responses under new conditions. The lin-log models also captured the dynamics, but not as well as the S-system models. A probable reason for the inferior performance of lin-log models is their intrinsic property of not representing situations well where variable concentrations are moderately small. [Copyright &y& Elsevier]

Abstract: A two-phase design approach is introduced to determine the optimal feed rate, fed glucose concentration and fermentation time to maximize protein productivity using recombinant Escherichia coli BL21 (pBAW2) strain. The first phase is applied to determine a primary S-system kinetic model using batch time-series data. Two runs were carried out in the second phase to achieve the maximum protein productivity for the fed-batch fermentation process. The computational results using the S-system kinetic model obtained from the second run are in better agreement with the experiments than those using the kinetic model obtained from batch time-series data. For cross-validation, two extra fed-batch experiments with different feed strategies were carried out for comparison with the optimal fed-batch result. From the experimental results, this approach could improve productivity by at least 3%. [Copyright &y& Elsevier]

Abstract: In this study, we have established a suitable model to describe the dynamic characteristic of an aspartase-overproducing Escherichia coli strain. A traditional Monod model and power-law models are respectively applied to describe the dynamic behaviors. When the model structure was selected, the parameter values of the model should be determined by the global/local optimization method. There are two major challenges, numerical integration for differential equations and finding global parameter values. In this study, we introduce hybrid differential evolution, which is a variant of genetic algorithms, to avoid obtaining a premature solution. In addition, we apply a modified collocation method to avoid numerical integration. The Monod''s model could only predict the growth characteristic of the recombinant E. coli, qualitatively. The S-system representation could suit for constructing the model structure of the microbial growth. [Copyright &y& Elsevier]

In this study, we consider a two-stage fermenter with cell recycling including an extractor to produce lactic acid. When the extractor was omitted, the proposed process was simplified to two special processes. Under the same operating conditions, we compared the overall lactic acid productivity and glucose conversion for the three processes. The proposed fermentation process was more efficient than the other processes. To simultaneously obtain the maximum productivity and conversion, the problem is formulated as a fuzzy multiobjective optimization problem. The fuzzy decision is introduced to convert the multiobjective fuzzy optimization problem into the fuzzy goal attainment problem. Hybrid differential evolution is applied to solve the fuzzy goal attainment problem to obtain a global Pareto solution. [Copyright &y& Elsevier]