Performance evaluation of an ANN–GA aided experimental modeling and optimization procedure for enhanced synthesis of marine biosurfactant in a stirred tank reactor

BACKGROUND: An improved resilient back‐propagation neural network modeling coupled with genetic algorithm aided optimization technique was employed for optimizing the process variables to maximize lipopeptide biosurfactant production by marine Bacillus circulans. RESULTS: An artificial neural network (ANN) was used to develop a non‐linear model based on a 2⁴ full factorial central composite design involving four independent parameters, agitation, aeration, temperature and pH with biosurfactant concentration as the process output. The polynomial model was optimized to maximize lipopeptide biosurfactants concentration using a genetic algorithm (GA). The ranges and levels of these critical process parameters were determined through single‐factor‐at‐a‐time experimental strategy. Improved ANN‐GA modeling and optimization were performed using MATLAB v.7.6 and the experimental design was obtained using Design Expert v.7.0. The ANN model was developed using the advanced neural network architecture called resilient back‐propagation algorithm. CONCLUSION: Process optimization for maximum production of marine microbial surfactant involving ANN‐GA aided experimental modeling and optimization was successfully carried out as the predicted optimal conditions were well validated by performing actual fermentation experiments. Approximately 52% enhancement in biosurfactant concentration was achieved using the above‐mentioned optimization strategy. © 2012 Society of Chemical Industry     

A hybrid deep learning framework driven by data and reaction mechanism for predicting sustainable glycolic acid production performance

Selective oxidation at low temperatures without alkali of biomass is a promising and sustainable avenue to manufacture glycolic acid (GA), a biodegradable functional material to protect the environment. However, producing glycolic acid with high selectivity and yield using the traditional research and development approach is time-consuming and labor-intensive. To this context, a hybrid deep learning framework driven by data and reaction mechanisms for predicting GA production was proposed, considering the lack of related reaction mechanisms in the machine learning algorithms. The proposed hybrid deep learning framework involves the kinetic reaction mechanism, catalyst properties, and reaction conditions. Results showed that the fully connected residual network exhibited superior performance (average R² = 0.98) for the prediction of conversion rate and product yields. Then, the multi-objective optimization and experimental verification guided the research are carried out. The experimental verification is comparable to the modeling results, with errors of less than 4% for conversion rate and GA yields. The life cycle assessment further identifies that using the optimized operating parameters, the fossil energy demand and greenhouse emissions have decreased by 2.96% and 3.00%, respectively. This work provides new insight and strategy to accelerate the engineered selective oxidation for desired GA production.     

Heterogeneous Reactor Modeling of an Industrial Multitubular Packed‐Bed Ethylene Oxide Reactor

The one‐dimensional heterogeneous model of an industrial multitubular packed‐bed ethylene oxide (EO) reactor was developed using the equation‐oriented platform Aspen Custom Modeler. Reactor operation was optimized in terms of maximized EO production and selectivity and enhanced safety related to the presence of oxygen in the EO reactor. Good agreement was found between the model results during validation against the available information under identical operating conditions. The model predicts the behavior of the EO reaction and demonstrates the extent of catalyst utilization with product distribution, product yield, by‐product formation, temperature and concentration profiles, over time and along the length of the reactor or catalyst bed. The model sensitivity studies compute the optimum feed flow, oxygen concentration, feed pressure, etc. and suggest the best operational philosophy.     

催化剂失活条件下的碳二加氢反应器模拟与优化

乙炔加氢反应催化剂的活性与选择性随着使用时间变化而变化,操作参数需要相应的改变从而使得收益最大化。因此本文采集工业数据,通过遗传算法拟合了反应动力学模型和失活反应模型的参数;比较了国内最普遍使用的两种催化剂的选择性和活性保持方面的性能;针对串联使用的两台装填不同催化剂的反应器组合,以运行周期、乙烯产率最优为评价标准,提出了反应器3种组合优化、负荷分配优化等优化策略。将优化策略应用于实际工业装置的分析与运行,乙烯产率和总选择性提高,效果得到验证。     

考虑不同产品选择性目标的乙醇胺反应精馏塔模拟与优化

提出以环氧乙烷和液氨为原料,水为催化剂,反应精馏塔合成乙醇胺的工艺。分别以一乙醇胺（MEA）和二乙醇胺（DEA）的选择性最大为目标,探讨了在同一反应精馏塔中实现不同产物选择性目标的可行性和可达到的最大选择性。研究在Aspen Plus模拟软件上进行,通过考察操作压力、水进料量、进料氨烷比、再沸比、环氧乙烷进料位置等参数对反应精馏的影响规律,取得实现不同产物选择性目标的参数优化调节方法。研究表明,大的氨烷比、水进料量和再沸比有利于提高MEA选择性,小的氨烷比、水进料量和再沸比则有利于提高DEA选择性,在优化条件下,MEA选择性可达70.30%,DEA选择性可达41.89%。与文献方法比较,采用反应精馏合成乙醇胺具有明显的优越性和操作柔性。     

Sustainable Dimethyl Carbonate Production from Ethylene Oxide and Methanol

A large-scale dimethyl carbonate (DMC) production process from ethylene oxide (EO), CO₂, and methanol was simulated and optimized. Unlike most industrial processes of DMC production, the direct conversion of EO and CO₂ to ethylene carbonate (EC) and EC transesterification to DMC were performed in a single reactor. The reaction volume and the reactor operating pressure were selected as decision variables and evaluated. The key performance parameters, e.g., conversion per pass and CO₂ intensity, were compared with conventional commercialized routes or novel promising processes in the literature.     

一种新的生产计划与催化裂化装置过程操作集成的闭环策略

Production planning models generated by common modeling systems do not involve constraints for process operations, and a solution optimized by these models is called a quasi-optimal plan. The quasi-optimal plan cannot be executed in practice some time for no corresponding operating conditions. In order to determine a practi- cally feasible optimal plan and corresponding operating conditions of fluidized catalytic cracking unit （FCCU）, a novel close-loop integrated strategy, including determination of a quasi-optimal plan, search of operating conditions of FCCU and revision of the production planning model, was proposed in this article. In the strategy, a generalized genetic algorithm （GA） coupled with a sequential process simulator of FCCU was applied to search operating conditions implementing the quasi-optimal plan of FCCU and output the optimal individual in the GA search as a final genetic individual. When no corresponding operating conditions were found, the final genetic individual based correction （FGIC） method was presented to revise the production planning model, and then a new quasi-optimal production plan was determined. The above steps were repeated until a practically feasible optimal plan and corresponding operating conditions of FCCU were obtained. The close-loop integrated strategy was validated by two cases, and it was indicated that the strategy was efficient in determining a practically executed optimal plan and corresponding operating conditions of FCCU.     

Simulation and optimization of ethanol amine production plant

An industrial Ethanol Amine (EA) production plant was simulated and optimized. Due to lack of accurate reaction rate information, the first step involved obtaining reliable kinetic data from the SRI (Stanford Research Institute) industrial database and calculation using error minimization method. In the next step, by implementing the obtained reaction kinetics the whole plant was simulated using Hysys software. Simulation results were compared with the SRI data and showed that there is acceptable agreement between simulation and the measured industrial data. In the next step of study by applying the gradient search (GS) optimization technique the plant was optimized using: feeding ammonia to ethylene oxide (EO) molar ratio, water flow rate in the feed stream, and reactor temperature as optimization variables. Employing process profit as objective function the optimal operating conditions were found to be: ammonia to EO ratio of 5 (mol/mol), water flow rate of 52.59 kg mol/hr and reactor temperature of 85 °C.     

Multistream heat exchangers: Equation‐oriented modeling and flowsheet optimization

Multistream heat exchangers (MHEXs), typically of the plate‐fin or spiral‐wound type, are a key enabler of heat integration in cryogenic processes. Equation‐oriented modeling of MHEXs for flowsheet optimization purposes is challenging, especially when streams undergo phase transformations. Boolean variables are typically used to capture the effect of phase changes, adding considerable difficulty to solving the flowsheet optimization problem. A novel optimization‐oriented MHEX modeling approach that uses a pseudo‐transient approach to rapidly compute stream temperatures without requiring Boolean variables is presented. The model also computes an approximate required heat exchange area to determine the optimal tradeoff between operating and capital expenses. Subsequently, this model is seamlessly integrated in a previously‐introduced pseudo‐transient process modeling and flowsheet optimization framework. Our developments are illustrated with two optimal design case studies, an MHEX representative of air separation operation and a natural gas liquefaction process. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1856–1866, 2015     

A Surrogate Integrated Production Modeling Approach to Long-Term Gas-Lift Allocation Optimization

The conventional static gas-lift allocation optimization approaches are not appropriate for long-term gas-lift projects. A good choice for long-term optimization should predict gas-lift performance dynamically as a function of production time and other variables. A good solution approach for problem is a hybrid of surrogate integrated production modeling and genetic algorithm (GA). Hybrid GAs have received significant interest in recent years and are being increasingly used to solve real-world problems. GA incorporates other techniques within its framework to produce a hybrid that reaps the best from the combination. This study discusses a new method known as surrogate integrated production modeling that uses an artificial neural network to predict gas-lift performance based on a database of oil production. Then, a hybrid of the neural network and GA is used for long-term gas-lift allocation optimization in a group of wells under real constraints.     

Modeling and optimization of cross-flow ultrafiltration using hybrid neural network-genetic algorithm approach

Precise modeling flux decline under various operating parameters in cross-flow ultrafiltration (UF) of oily wastewaters and afterward, employing an appropriate optimization algorithm in order to optimize operating parameters involved in the process model result in attaining desired permeate flux, is of fundamental great interest from an economical and technical point of view. Accordingly, this current research proposed a hybrid process modeling and optimization based on computational intelligence paradigms where the combination of artificial neural network (ANN) and genetic algorithm (GA) meets the challenge of specified-objective based on two steps: first the development of bio-inspired approach based on ANN, trained, validated and tested successfully with experimental data collected during the polyacrylonitrile (PAN) UF process to treat the oily wastewater of Tehran refinery in a laboratory scale in which the model received feed temperature (T), feed pH, trans-membrane pressure (TMP), cross-flow velocity (CFV), and filtration time as inputs; and gave permeate flux as an output. Subsequently, the 5-dimensional input space of the ANN model portraying process input variables was optimized by applying GA, with a view to realizing maximum or minimum process output variable. The results obtained validate the estimates of the ANN–GA technique with a good accuracy. Finally, the relative importance of the controllable operation factors on flux decline is determined by applying the various correlation statistic techniques. According to the result of the sensitivity analysis based on the correlation coefficient, the filtration time was the most significant one, followed by T, CFV, feed pH and TMP.     

Operational optimization of a simulated atmospheric distillation column using support vector regression models and information analysis

Like any other production processes, atmospheric distillation of crude oil is too complex to be accurately described with first principle models, and on-site experiments guided by some statistical optimization method are often necessary to achieve the optimum operating conditions. In this study, the design of experiment (DOE) optimization procedure proposed originally by Chen et al. (1998) and extended later by Chu et al. (2003) has been revised by using support vector regression (SVR) to build models for target processes. The location of future experiments is suggested through information analysis which is based on SVR models for the performance index and observed variables and reduces significantly the number of experiments needed. A simulated atmospheric distillation column (ADC) is built with Aspen Plus (version 11.1) for a real operating ADC. Kernel functions and parameters are investigated for SVR models to represent suitably the behavior of the simulated ADC. To verify the effectiveness of the revised DOE optimization procedure, three case studies are carried out: (1) The modified Himmelblau function is minimized under a circle constraint; (2) the net profit of the simulated ADC is maximized with all the 15 controlled variables free for adjusting in their operational ranges; (3) the net profit of the simulated ADC is maximized with fixed production rates for the three side-draws.     

乙炔加氢反应过程混合建模与优化

针对传统单一建模方法所构建的乙炔加氢反应器数学模型存在预测性能无法满足工业实际应用需求的问题,提出了一种机理与神经网络嵌套的建模方法,充分利用机理模型包含的能质约束信息降低神经网络模型的约束违反度,得到了能够良好描述实际工业乙炔加氢反应过程特性的混合模型。基于反应器混合模型,研究了以运行效益为目标函数的优化问题。主要决策变量包括：一段反应器进料中氢气与乙炔的摩尔比（R _H/A）、进料温度和反应器运行周期等几个关键参数。针对反应器长期运行后,催化剂活性降低造成的处理能力下降的问题,提出了反应温度补偿机制和R _H/A并行调节的运行优化策略,并采用序列法对反应器运行周期进行离散化处理。通过引入差异化变异策略、潜在解替代策略对两阶段差分算法进行改进,采用增量式编码法结合改进两阶段差分算法,对优化问题进行求解。结果证实了优化策略与改进算法的有效性,并据此确定了反应器最佳运行方案。     

A hybrid numerical‐symbolic solving strategy for equation‐oriented process simulation and optimization

The equation‐oriented (EO) and sequential modular (SM) methods are two typical approaches for numerical process simulation and optimization. For a large‐scale system, the EO method usually suffers from difficulties in variable initialization. The SM method, conversely, can suffer from slow convergence and requires experience in choosing appropriate tear variables. In this article, a novel strategy combining numerical and symbolic approaches is proposed for solving process systems represented by polynomials. First, a digraph method is developed to identify the subset of equations that should be solved simultaneously. Then, a symbolic computation method based on Gröbner basis is proposed to reformulate the simultaneous equations as a completely sequential model with a triangular structure. Last, the reformulated model is solved sequentially without any iterative tearing process. The case studies show that the proposed strategy can significantly improve the solving efficiency and robustness for process simulation and optimization. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2764–2780, 2017     

Biodegradable iron chelate for H2S abatement: Modeling and optimization using artificial intelligence strategies

A batch reactor process for the abatement of a common pollutant, namely, H₂S using Fe³⁺-malic acid chelate (Fe³⁺-MA) catalyst has been developed. Further, process modeling and optimization was conducted in the three stages with a view to maximize the H₂S conversion: (i) sensitivity analysis of process inputs was performed to select the most influential process operating variables and parameters, (ii) an artificial neural network (ANN)-based data-driven process model was developed using the influential process variables and parameters as model inputs, and H₂S conversion (%) as the model output, and (iii) the input space of the ANN model was optimized using the artificial immune systems (AIS) formalism. The AIS is a recently proposed stochastic nonlinear search and optimization method based on the human biological immune system and has been introduced in this study for chemical process optimization. The AIS-based optimum process conditions have been compared with those obtained using the genetic algorithms (GA) formalism. The AIS-optimized process conditions leading to high (≈97%) H₂S conversion, were tested experimentally and the results obtained thereby show an excellent match with the AIS-maximized H₂S conversion. It was also observed that the AIS required lesser number of generations and function evaluations to reach the convergence when compared with the GA.     

分批补料反应过程的非固定终端经济优化控制

针对批次生产周期不确定问题,提出一种非固定终端的经济优化控制方法。首先采用经济模型预测控制方法,用收益最大化的经济型目标函数代替终端约束,并将批次生产周期纳入被优化变量,建立动态经济优化问题,并通过对每个控制变量进行有差异的参数化,将动态优化问题转化为非线性规划（NLP）问题;然后使用内点罚函数法求解含非线性约束的优化问题,得到的最优控制序列和最佳批次生产周期,可将不确定扰动带来的损失降低到最小。其次采用非固定预测时域的滚动时域控制方法,不仅提高多变量系统的协同控制能力,而且根据实时预测终端产品产量不断优化更新关键操纵变量的控制分段函数的分割数及控制序列,从而可灵活优化操纵变量和操作时间的轨迹。最后在苯胺加氢过程上进行了批次优化控制性能测试,测试结果表明,非固定终端的经济优化控制从批次的总生产效益角度来优化每个批次生产的操作条件,实现批次反应过程生产时间与经济效益的最优化管理。     

Study on Optimal Strategy of Grade Transition in Industrial Fluidized Bed Gas-Phase Polyethylene Production Process

A model of grade transition is presented for a commercialized fluidized bed gas-phase polyethylene production process. The quantity of off-specification product and the time of grade transition can be minimized by the optimization of operating variables, such as polymerization temperature, the ratio of hydrogen to ethylene, the ratio of co-monomer to ethylene, feed rate of catalyst, and bed level. A new performance index, the ratio of melt flow (MFR), is included in the objective function, for restraining the sharp adjustment of operation variables and narrowing the distribution of molecular weight of the resin. It is recommended that catalyst feed rate and bed level are decreased in order to reduce the grade transition time and the quantity of off-specification product. This optimization problem is solved by an algorithm of sequential quadratic programming (SQP) in MATLAB. There is considerable difference between the forward transition and reverse transition of grade with regard to the operating variab     

A novel approach to the design and operation scheduling of heterogeneous catalytic reactors

A number of studies have been conducted to reduce the overall level of catalyst deactivation in heterogeneous catalytic reactors, and improve the performance of reactors, such as yield, conversion or selectivity. The methodology generally includes optimization of the following: (1) operating conditions of the reaction system, such as feed temperature, normal operating temperature, pressure, and composition of feed streams; (2) reactor design parameters, such as dimension of the reactor, side stream distribution along the axis of the reactor beds, the mixing ratio of inert catalyst at each bed; and (3) catalyst design parameters, such as the pore size distribution across the pellet, active material distribution, size and shape of the catalyst, etc. Few studies have examined optimization of the overall catalyst reactor performance throughout the catalyst lifetime, considering catalyst deactivation. Furthermore, little attention has been given to the impact of various configurations of reactor networks and scheduling of the reactor operation (i.e., online and offline-regeneration) on the overall reactor performance throughout the catalyst lifetime. Therefore, we developed a range of feasible sequences of reactors and scheduling of reactors for operation and regeneration, and compared the overall reactor performance of multiple cases. Furthermore, a superstructure of reactor networks was developed and optimized to determine the optimum reactor network that shows the maximum overall reactor performance. The operating schedule of each reactor in the network was considered further. Lastly, the methodology was illustrated using a case study of the MTO (methanol to olefin) process.     

RBF-CSR-EHCGA模型及其在脉冲萃取中的应用

径向基函数-循环子空间回归（RBF-CSR）是一种有效的非线性网络模型,以高斯条为基函数,性能更优,但其参数多,且难以选定,将显著影响模型性能.为此,本文提出基于优进策略的混合编码遗传算法（EHCGA）,以不同的方式为各类参数编码,并引入确定性的Powell算子,提高全局搜优效率.EHCGA算法以模型预报性能为目标,优选参数,以此建立RBF-CSR-EHCGA模型,它的预报精度高、稳定性良好.已成功应用于回收己内酰胺的脉冲萃取过程建模,效果良好,明显优于其他网络模型,也优于近似机理模型.     

New Approach in the Prediction of RDC Liquid‐Liquid Extraction Column Parameters

The liquid‐liquid extraction process is well‐known for its complexity and often entails intensive modeling and computational efforts to simulate its dynamic behavior. This paper presents a new application of the Genetic Algorithm (GA) to predict the modeling parameters of a chemical pilot plant involving a rotating disc liquid‐liquid extraction contactor (RDC). In this process, the droplet behavior of the dispersed phase has a strong influence on the mass transfer performance of the column. The mass transfer mechanism inside the drops of the dispersed phase was modeled by the Handlos‐Baron circulating drop model with consideration of the effect of forward mixing. Using the Genetic Algorithm method and the Numerical Analysis Group (NAG) software, the mass transfer and axial dispersion coefficients in the continuous phase in these columns were optimized. In order to obtain the RDC column parameters, a least‐square function of differences between the simulated and experimental concentration profiles (SSD) and 95 % confidence limit in the plug flow number of the transfer unit prediction were considered. The minus 95 % confidence limit and sum of square deviations for the GA method justified it as a successful method for optimization of the mass transfer and axial dispersion coefficients of liquid‐liquid extraction columns.