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.     

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.     

Multi‐objective optimization of reactive extrusion by genetic algorithms

In reactive extrusion processes for polymerization, a multi‐objective optimization model maximizing the monomer conversion whilst ensuring the low energy consumption was constructed. The selections of reactive processing conditions could be set automatically using an optimization methodology based on genetic algorithms coupled with the numerical simulation routines. Various case studies were discussed. Comparison with experimental data indicates that the design of processing conditions can be performed according to the prespecified objectives. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41862.     

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 %.     

Optimization strategy of the multihole products weld lines based on the multi‐objective evaluation system and kriging surrogate model

The plastic multihole products are widely used in Household Appliance and Automobile industry. The densely distributed weld lines resulting from the multiaperture structure seriously damage the surface appearance and mechanical properties of the products. However, there are relatively few studies on the optimization of weld lines of the plastic multihole products. Therefore, a multihole plate was taken as an example to exploit an optimization strategy for this kind of defects. The multi‐objective evaluation system was established to assess bonding quality of weld lines based on Numerical Simulation. And a multi‐objective optimization methodology was presented to efficiently optimize the process parameters. First, back propagation neural networks and Kriging method were combined to build the surrogate models based on the experiments arranged by Latin hypercube sampling. Second, a systematic test strategy was proposed to ensure high accuracy of the surrogate model. Then, the pareto optimal solution was obtained by integrating the surrogate model and Non‐dominated Sorting Genetic Algorithm II. Finally, the simulated and practical confirmation experiments indicate that bonding quality of weld lines can be effectively characterized and significantly improved by the multi‐objective evaluation system and the multi‐objective optimization methodology, respectively. POLYM. ENG. SCI., 59:781–790, 2019. © 2018 Society of Plastics Engineers     

Optimal design of batch‐storage network considering ownership

This article develops a model of multi‐national supply chain activities, which incorporates currency storage units to manage currency flows associated with activities such as raw material procurement, processing, inventory control, transportation, and finished product sales. The core contribution of this model is that it facilitates the quantitative investigation of the influence of macroscopic economic factors such as ownership on supply chain operational decisions. The supply chain system is modeled as a batch‐storage network with recycle streams. The supply chain optimization problem is posed with the objective of minimizing the opportunity costs of annualized capital investments and currency/material inventories, while taking into account the benefit to stockholders in the numeraire currency. The major constraints on the optimization are that the material and currency storage units must not be depleted. A production and inventory analysis formulation (the periodic square wave model) provides useful expressions for the upper and lower bounds and for the average levels of the currency and material inventory holdings. The expressions for the Kuhn‐Tucker conditions of the optimization problem are reduced to a subproblem that allows development of analytical lot‐sizing equations. The lot sizes of procurement, production, transportation, and financial transactions can be determined in closed form once the average flow rates are known. The key result we obtain is that optimal value of the economic order quantity changes substantially with variation in ownership, thus showing quantitatively that ownership structure does impact plant operation. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2418–2425, 2018     

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.     

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.     

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.     

Multi‐objective optimization superimposed model‐based process design of an enzymatic hydrolysis process

The concepts of green process engineering and rigorous model‐based approaches have proven to be highly beneficial in process engineering. Although a combination of these two principles thus appears extremely promising, it is not found very commonly in literature. The very high complexity resulting from this combination poses great challenges for the process design and design engineers. Therefore, this work presents an innovative methodology for the model‐based process design with superimposed multi‐objective optimization for an exemplary process. This process for the enzymatic hydrolysis of fatty acid methyl ester combines several aspects of green process engineering and represents an exemplary process with an enzymatic liquid‐liquid‐solid reaction system. The optimization results based on operating and investment costs reveal important insights on the exemplary process and highlight the great advantages of the developed methodology as a profound basis for academic and industrial process design purposes. © 2017 American Institute of Chemical Engineers AIChE J, 63: 1974–1988, 2017     

Dynamic real‐time optimization with closed‐loop prediction

Process plants are operating in an increasingly global and dynamic environment, motivating the development of dynamic real‐time optimization (DRTO) systems to account for transient behavior in the determination of economically optimal operating policies. This article considers optimization of closed‐loop response dynamics at the DRTO level in a two‐layer architecture, with constrained model predictive control (MPC) applied at the regulatory control level. A simultaneous solution approach is applied to the multilevel DRTO optimization problem, in which the convex MPC optimization subproblems are replaced by their necessary and sufficient Karush–Kuhn–Tucker optimality conditions, resulting in a single‐level mathematical program with complementarity constraints. The performance of the closed‐loop DRTO strategy is compared to that of the open‐loop prediction counterpart through a multi‐part case study that considers linear dynamic systems with different characteristics. The performance of the proposed strategy is further demonstrated through application to a nonlinear polymerization reactor grade transition problem. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3896–3911, 2017     

Optimal supply chain design and operations under multi‐scale uncertainties: Nested stochastic robust optimization modeling framework and solution algorithm

Although strategic and operational uncertainties differ in their significance of impact, a “one‐size‐fits‐all” approach has been typically used to tackle all types of uncertainty in the optimal design and operations of supply chains. In this work, we propose a stochastic robust optimization model that handles multi‐scale uncertainties in a holistic framework, aiming to optimize the expected economic performance while ensuring the robustness of operations. Stochastic programming and robust optimization approaches are integrated in a nested manner to reflect the decision maker's different levels of conservativeness toward strategic and operational uncertainties. The resulting multi‐level mixed‐integer linear programming model is solved by a decomposition‐based column‐and‐constraint generation algorithm. To illustrate the application, a county‐level case study on optimal design and operations of a spatially‐explicit biofuel supply chain in Illinois is presented, which demonstrates the advantages and flexibility of the proposed modeling framework and efficiency of the solution algorithm. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3041–3055, 2016     

Dynamic delayed detached eddy simulation of a multi‐inlet vortex reactor

The multi‐inlet vortex reactor (MIVR) is used for flash nanoprecipitation to manufacture functional nanoparticles. A validated computational fluid dynamics model is needed for the design, scale‐up, and optimization of the MIVR. Unfortunately, available Reynolds‐averaged Navier‐Stokes methods are unable to accurately model the highly swirling flow in the MIVR. Large‐eddy simulations (LES) are also problematic, as excessively fine grids are required to accurately model this flow. These dilemmas led to the application of the dynamic delayed detached eddy simulation (DDES) method to the MIVR. In the dynamic DDES model, the eddy viscosity has a form similar to the Smagorinsky sub‐grid viscosity in LES, which allows the implementation of a dynamic procedure to determine its model coefficient. Simulation results using the dynamic DDES model are found to match well with experimental data in terms of mean velocity and turbulence intensity, suggesting that the dynamic DDES model is a good option for modeling the turbulent swirling flow in the MIVR. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2570–2578, 2016     

Multi‐Objective Optimization in Distillation Unit: A Case Study

Increasing social pressure and strict legislations have resulted in changing the approach of traditional design practices to incorporate multiple objectives in the design of process plants. Distillation is one of the major operations in the chemical process industry that is widely used for purifying products or recovering solvents or separation of valuable reactants from waste stream. In this paper, a procedure for multi‐objective optimization is discussed with the help of a distillation unit from hydrocarbon recovery plant of a distillate fraction process. The procedure developed here consists of four stages and is based on current design tools. The aim is to support decisions during design phase and optimize the process variables in order to generate a process with improved economics along with satisfaction of environmental objectives. Total potential environment impact and total annualized cost are used as indicator for environmental and economic objectives, respectively.     

Preferential crystallization: Multi‐objective optimization framework

A four objective optimization framework for preferential crystallization of D‐L threonine solution is presented. The objectives are maximization of average crystal size and productivity, and minimization of batch time and the coefficient of variation at the desired purity while respecting design and operating constraints. The cooling rate, enantiomeric excess of the preferred enantiomer, and the mass of seeds are used as the decision variables. The optimization problem is solved by using adaptation of the nondominated sorting genetic algorithm. The results obtained clearly distinguish different regimes of interest during preferential crystallization. The multi‐objective analysis presented in this study is generic and gives a simplified picture in terms of three zones of operations obtained because of relative importance of nucleation and growth. Such analysis is of great importance in providing better insight for design and decision making, and improving the performance of the preferential crystallization that is considered as a promising future alternative to chromatographic separation of enantiomers. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Robust batch‐to‐batch optimization in the presence of model‐plant mismatch and input uncertainty

In model‐based optimization in the presence of model‐plant mismatch, the set of model parameter estimates which satisfy an identification objective may not result in an accurate prediction of the gradients of the cost‐function and constraints. To ensure convergence to the optimum, the predicted gradients can be forced to match the measured gradients by adapting the model parameters. Since updating all available parameters is impractical due to estimability problems and overfitting, there is a motivation for adapting a subset of parameters for updating the predicted outputs and gradients. This article presents an approach to select a subset of parameters based on the sensitivities of the model outputs and of the cost function and constraint gradients. Furthermore, robustness to uncertainties in initial batch conditions is introduced using a robust formulation based on polynomial chaos expansions. The improvements in convergence to the process optimum and robustness are illustrated using a fed‐batch bioprocess. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2660–2670, 2017     

Kilogram‐Scale Asymmetric Ruthenium‐Catalyzed Hydrogenation of a Tetrasubstituted Fluoroenamide

Ruthenium‐catalyzed asymmetric homogeneous hydrogenation (AHH) is used as the key step of a multi‐kilogram scale synthesis of an enantiomeric fluoropiperidine. The AHH of a tetrasubstituted β‐fluoroenamide is carried out under mild conditions using a Ru/Josiphos catalyst with high ee (98%).