Robust Optimal Operation of Two‐Chamber Microbial Fuel Cell System Under Uncertainty: A Stochastic Simulation Based Multi‐Objective Genetic Algorithm Approach

This investigation is performed to study the optimal operation decision of two‐chamber microbial fuel cell (MFC) system under uncertainty. To gain insight into the mechanism of uncertainty propagation, a Quasi‐Monte Carlo method‐based stochastic analysis is conducted not only to elucidate the effect of each uncertain parameter on the variability of power density output, but also to illustrate the interactive effects of the all uncertain parameters on the performance of MFC. Moreover, a systematic stochastic simulation‐based multi‐objective genetic algorithm framework is proposed to identify a set of Pareto‐optimal robust operation strategies, which is helpful to provide an imperative insight into the relationship between the mean and standard deviation of output power density. The results indicate that (1) the coefficient of variance (COV) value of output power density has a linear relationship with the COV value of each uncertainty parameter as well as all interactive parameters; and (2) a significant performance improvement with respect to both mean and standard deviation of power density is observed by implementing the multi‐objective robust optimization. These results thus validate that the proposed uncertainty analysis and robust optimization framework provide a promising tool for robust optimal design and operation of fuel cell systems under uncertainty.     

Multi‐Objective Optimization of Interior Ballistic Performance Using NSGA‐II

This paper investigates an interior ballistic design with equal and unequal web thicknesses of seven‐perforation propellant grains using optimization methods. In order to reveal the influence of the web thickness of the propellant grains on the overall interior ballistic performance, burning seven‐perforation propellant grains with both equal and unequal web thickness is modeled. A currently popular evolution algorithm (EA) is used to compare two charge shapes, and to seek which one could achieve the optimal ballistic performance. Complete optimization of the interior ballistic performance is a complex process in view of the conflicting objectives to be achieved and a solution to such problems is sought by converting them into a single composite objective and using many tedious measurements. In this paper, a true multi‐objective optimization of the interior ballistic charging design is carried out by considering three objectives simultaneously. The non‐dominated sorting genetic algorithm version II (NSGA‐II) is used to solve this multi‐objective optimization problem (MOP). In order to check its implementation, both the conventional optimization algorithm‐hill climbing method (HCM) and NSGA‐II are used to solve the same single objective problem. The NSGA‐II used to capture the full Pareto‐optimal front is capable of identifying the trade‐off among the conflicting objectives thereby providing alternative useful designs for a designer. Furthermore, for seven‐perforation propellant grains, the results of using equal web thickness are compared with those of unequal web thickness, and it is shown that the two charge shapes produce no distinct difference in the interior ballistic performance.     

Using NSGA‐II and TOPSIS Methods for Interior Ballistic Optimization Based on One‐Dimensional Two‐Phase Flow Model

In order to meet the design demands of new gun systems or new types of projectiles, the interior ballistic charge design seems especially important. In this paper, a one‐dimensional two‐phase flow model is presented. The model describes the transient combustion of granular propellants in a gun, and pressure waves are considered as an objective. This study adopts a hybrid method to solve the problem. In the first stage, the non‐dominated sorting genetic algorithm (NSGA‐II) with “a “filter” is employed to approximate a set of Pareto‐optimal solutions. In the subsequent stage, a multi‐attribute decision‐making (MADM) approach is adopted to rank these solutions from the best to the worst. The ranking of Pareto‐optimal solutions is based on the technique ordered preference by similarity to ideal solution (TOPSIS) method. In TOPSIS method each objective needs a corresponding weight coefficient, and a practical problem is introduced. Both the entropy method and linear analysis method are adopted to get two sets of weights for the objectives, respectively. The two pairs of final, best compromise solutions are compared for satisfying the designer’s aim. For the analysis of the results, a two‐phase flow interior ballistic model for design optimization is established, and the hybrid approach could get a reasonable design scenario.     

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.     

Multi‐Objective Optimization of Pseudo‐Dynamic Operation of Naphtha Pyrolysis by a Surrogate Model

A simple pseudo‐dynamic surrogate model is developed in the framework of the state space model with the feed‐forward neural network to replace the complex free radical pyrolysis model. The surrogate model is then applied to investigate the multi‐objective optimization of two key performance objectives with distinct contradiction: the mean yields of key products and the day mean profits. The ?‐constraint method is employed to solve the multi‐objective optimization problem, which provides a broad range of operation conditions depicting tradeoffs of both key objectives. The Pareto‐optimal frontier is successfully obtained and five selected cases on the frontier are discussed, suggesting that flexible operations can be performed based on industrial demands.     

Computer‐Aided Molecular Design of Environmentally Friendly Solvents for Separation Processes

This review paper presents an optimization technology for the computer‐aided molecular design of environmentally friendly solvents. The approach combines a stochastic optimization method and group contribution methods (GC‐methods) to design solvents with desirable physicochemical and environmental properties. A simulated annealing algorithm is used to investigate feasible molecular structures. The main objective method is adopted to balance the multi‐objective functions. One property is chosen as the main objective function, while the other properties are considered as constraints, and thus, the multi‐objective problem is transformed into a single objective one. The optimal solution is a set of molecules satisfying the formulated target. The properties of each molecule are evaluated through GC‐methods, including pure component properties, mixture properties and environmentally properties. Finally, the proposed methodology is illustrated with several examples of industrial separations.     

Interactive Multi‐objective Dynamic Optimization of Bioreactors under Parametric Uncertainty

Model‐based optimization techniques play a key role in achieving a sustainable operation of biochemical processes. Models are an approximation of the real process under study, hence, uncertainty is inherently present and for a sustainable process operation this uncertainty should be accounted for. In practice, optimality with respect to different conflicting objectives is required and multi‐objective optimization is a valuable tool. In this article the sigma point approach is applied to account for parametric uncertainty in the frame of interactive multi‐objective bioprocess optimization.     

板翅式换热器模糊优化设计

应用结构模糊优化理论对板翅式换热器进行多目标优化设计；给出了构造隶属函数及确定目标函数权重的方法，通过线性加权法将多目标化为单目标求解。应用此软件设计了一台换热器，其实际使用结果表明完全达到设计要求。     

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.     

300个农药制剂对蜜蜂的急性经口毒性

[目的]蜜蜂是一种重要的传粉昆虫,近年来世界各地出现了严重的种群衰退现象,原因之一是农业生产过程中使用了大量农药.采用摄入法测定f300个农药制剂对蜜蜂的急性经口毒性,旨在了解我国常用农药对蜜蜂的毒风险情况.[结果]结果表明:对蜜蜂的急性经口毒性表现剧毒、高毒、中毒、低毒的农药制剂分别占14.7%、35.6%、10.7%、39%,高毒和剧毒农药产品达50%以上,但这些制剂主要为杀虫剂,占整个杀虫剂的74.1%,杀菌剂和除草剂对蜜蜂的毒性相对较低;对于相同有效成分的不同制剂之间,由于有效成分含量、助剂、剂型及加工工艺等的差异,从而对蜜蜂的毒性出现有差异的情况;此外,部分生物源农药也对蜜蜂具有较高毒性.[结论]我国农药制剂产品中,对蜜蜂表现高急性毒性的产品比例较大,在农业生产过程中,尤其是使用杀虫剂时尽量避免或减轻其对蜜蜂造成的影响.     

Precursor Selection for Sol‐Gel Synthesis of Titanium Carbide Nanopowders by a New Intuitionistic Fuzzy Multi‐Attribute Group Decision‐Making Model

This article develops a new intuitionistic fuzzy multi‐attribute group decision‐making model to solve precursor selection problems. The proposed model is based on a hybridization of intuitionistic fuzzy sets theory, grey relational analysis and technique for order performance by similarity to ideal solution method. To better describe the uncertain decision environment, multi‐attribute group decision‐making method with completely unknown weights of both experts and attributes is proposed in an intuitionistic fuzzy setting. Finally, the selection of the carbon source in synthesis of nanocrystalline titanium carbide powders by sol‐gel route as an application example is solved by the proposed model, and the implementation of the model is illustrated completely.     

Multi‐Objective Optimization of a Cross‐Flow Plate Heat Exchanger Using Entropy Generation Minimization

Multi‐objective optimization of a cross‐flow plate fin heat exchanger (PFHE) by means of an entropy generation minimization technique is described. Entropy generation in the PFHE was separated into thermal and pressure entropy generation as two objective functions to be minimized simultaneously. The Pareto optimal frontier was obtained and a final optimal solution was selected. By implementing a decision‐making method, here the LINMAP method, the best trade‐off was achieved between thermal efficiency and pumping cost. This approach led to a configuration of the PFHE with lower magnitude of entropy generation, reduced pressure drop and pumping power, and lower operating and total cost in comparison to single‐objective optimization approaches.     

Solving Multi‐objective MILP Problems in Process Synthesis using the Multi‐Criteria Branch and Bound Algorithm

The paper briefly describes the problem of process synthesis in the area of chemical engineering, and suggests its formulation as a Multi‐Objective Programming problem. Process synthesis optimization is usually modeled as Mixed Integer Linear Programming (MILP) or Mixed Integer Non‐Linear Programming (MINLP) with an economic objective function. We claim that incorporating more criteria (e.g., environmental criteria) in this kind of combinatorial optimization problem offers the decision makers the opportunity to refine their final decision by examining more than one solution (a set of efficient or Pareto optimal solutions instead of one optimal solution). For solving the multi‐objective process synthesis problem, an improved version of the Multi‐Criteria Branch and Bound (MCBB) algorithm, which has been developed by the same authors, is used. MCBB is a vector maximization algorithm capable of deriving all efficient points (supported and unsupported), for small and medium sized Multi‐Objective MILP problems. The application of MCBB in two examples from process synthesis is also presented.     

A Comparative Study of Fuzzy Techniques to Handle Uncertainty: An Industrial Grinding Process

Fuzzy‐based approaches like fuzzy chance constrained programming (FCCP) and fuzzy expected value model (FEVM) have been applied to a multi‐objective optimization problem of the industrial grinding process to carry out the uncertainty analysis. Results are compared with respect to the power of risk averseness adopted in the approaches used. The extent of constraint satisfaction due to the presence of uncertain parameters can be accommodated assuming credibility of constraint satisfaction under the FCCP framework whereas the robust set of parameters in the FEVM approach is determined by considering the expectation terms for objectives and constraints. Nonlinear relation of uncertain parameters has been handled by adopting simulation‐based approaches while computing the credibility. These approaches are very generic and can be applied for the study of parametric sensitivity for any process model in a novel manner.     

Process Analysis and Multi‐Objective Optimization of Ionic Liquid‐Containing Acetonitrile Process to Produce 1,3‐Butadiene

Using ionic liquid (IL) [C₂MIM][PF₆] as an additive could remarkably improve the performance of the acetonitrile (CAN) process, which is the most widely used distillation process to produce 1,3‐butadiene (1,3‐BT). In this work, a rigorous simulation of a new IL process to produce 1,3‐BT was carried out to evaluate the performance of IL additive on an industrial scale, using UNIFAC as the global thermodynamic model. Based on the simulation models, some key operation parameters, such as solvent ratio and reflux ratio, were determined by sensitivity analysis. Furthermore, a multi‐objective optimization was proposed and performed considering both the energy consumption and environmental impact (green degree) of the new process. A nonlinear mathematical model was established to express this multi‐objective optimization problem, which includes six decision variables and involves maximizing the green degree of the process, the purity and the recovery of 1,3‐BT, and minimizing the energy consumption of the process. The optimization results showed that the energy consumption of the IL‐containing process could be reduced by 22 % and that its green degree could be improved by 9.2 %.     

Response Surface Optimization of Dysprosium Extraction Using an Emulsion Liquid Membrane Integrated with Multi‐Walled Carbon Nanotubes

A new emulsion liquid membrane was prepared for dysprosium (Dy) extraction from aqueous solution using multi‐walled carbon nanotubes (MWCNTs). The influence of MWCNT concentration, carrier and surfactant concentration, stirring speed, feed‐phase pH, and internal phase concentration and their interactive effects were studied. A regression model for Dy extraction was developed and the parameters were optimized by response surface methodology. The extent of extraction increases with higher MWCNT concentration up to a certain level. The Dy extraction through the liquid membrane containing MWCNT improves with time. Moreover, the overall mass transfer coefficient was enhanced in the presence of MWCNT due to the formation of a more stable emulsion and liquid membrane.     

Probabilistic Multiobjective Operation Management of MicroGrids With Hydrogen Storage and Polymer Exchange Fuel Cell Power Plants

This paper models and solves the operation management problem of MicroGrids (MGs) including cost and emissions minimization under uncertain environment. The proposed model emphasizes on fuel cells (FCs) as a prime mover of combined heat and power (CHP) systems. An electro‐chemical model of the proton exchange membrane fuel cell (PEMFC) is used and linked to the daily operating cost and emissions of the MGs. A reformer is considered to produce hydrogen for PEMFCs. Moreover, in high thermal load intervals, in order to make the MG more efficient, a part of produced hydrogen is stored in a hydrogen tank. The stored hydrogen can be reused by PEMFCs to generate electricity or be sold to other hydrogen consumers. A probabilistic optimization algorithm is devised which consists of 2m + 1 point estimate method to handle the uncertainty in input random variables (IRVs) and a multi‐objective Self‐adaptive Bee Swarm Optimization (SBSO) algorithm to minimize the cost and emissions simultaneously. Several techniques are proposed in the SBSO algorithm to make it a powerful black‐box optimization tool. The efficiency of the proposed approach is verified on a typical grid‐connected MG with several distributed energy sources.     

An optimization study on the pyrolysis of polystyrene in a batch reactor

The pyrolysis process of polystyrene (PS) has been investigated to find optimal temperature profiles which minimize the reaction time and the reaction energy required for a given conversion in a batch reactor. Assuming that the fragmentation of PS in pyrolysis is described by the mechanism of random and/or specific degradations, we used a continuous kinetic model for solving three moment equations to determine the transient change of molecular weight distributions (MWD) of the polymers. We then converted this independent-variable minimization problem using a coordinate transformation to a dependent-variable minimization problem that yields the optimal temperature profiles as its solution. The optimization results obtained in this study encompass the cases of different objective functions which cover minimum reaction time, minimum energy consumed, or any combination of these. It has turned out that maintaining the reaction temperature constant at an optimal level is the best solution in this optimization problem. An economic cost function also has been introduced as the third objective function to be minimized in addition to the reaction time and the reaction energy. This new function can serve as a convenient measure to judge the performance of the pyrolysis process minimizing the involved cost.     

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.     

Identification and formalization of knowledge for coloring qualitative geospatial data

Creating a satisfying qualitative color scheme from scratch may be difficult for novice mapmakers and experts. A probability‐based method is proposed to identify knowledge regarding qualitative color selection from readily available color schemes and formalize the discovered knowledge to assist in color creation. An unsupervised method to extract the general trends of color selection is presented, and the issue of qualitative color selection is translated into a multi‐constraint optimization problem. A feasible solution for achieving the global optimum is then provided. A probability‐based method is also proposed to match abstract color values with specific mapping features. This proposed approach is evaluated in a case study. The results of the case study suggest that the proposed method allows users to create qualitative color schemes more efficiently and confidently.