活性污泥法脱氮除磷工艺的优化设计

利用活性污泥2D模型对城市污水厂脱氮除磷工艺进行优化设计,构建A²/O工艺的仿真模型,通过模型校正对工艺参数进行优化,并将优化设计与传统设计法和试算法进行比较。结果表明,优化模型得出的模拟结果与实验测定值基本相吻合。优化设计法得出的污水厂基建费用和运行成本与其他两种方法相比,都有了很大的降低,虽然出水水质略有下降,但仍满足国家排放一级B标准。活性污泥2D模型可以对污水厂进行优化设计和控制。在满足出水水质前提下,降低污水厂的费用,并对以后的工艺设计提供理论指导。     

活性污泥工艺传统设计方法中存在问题的探讨

污泥负荷法和泥龄法是活性污泥工艺设计的主要计算方法,这2种设计方法偏于安全、保守,以经验为主,没有反映出活性污泥工艺中生物反应机理,缺少理论支持,适用范围窄,而数学模型法才是活性污泥法设计的发展趋势,利用数学模型进行活性污泥工艺设计可以有2种方法选用:试算法和优化法。     

RT培司装置尾气吸收废酸处理工艺研究

研究RT培司（对氨基二苯胺）装置全工段尾气吸收废酸（简称废酸）的处理工艺。采用电气石非均相Fenton法对废酸进行处理,确定优化工艺条件为：反应温度　20～25 ℃,过氧化氢用量　30.27 g·L^-1,电气石用量　240 g·L^-1, 反应时间　10 h,在该优化工艺条件下废酸化学需氧量（COD）去除率约为35%。采用单级好氧活性污泥法处理废酸,COD去除率为62.63%。采用Fenton-好氧活性污泥法联合工艺可以有效处理废酸,COD去除率为98.92%,出水COD为 1 230 mg·L^-1,出水可接入污水管网进行进一步处理。设计模试装置,对Fenton-好氧活性污泥法联合工艺进行模试放大试验,废酸COD去除率为98.47%,出水COD为1 757 mg·L^-1,可进一步探索工业化试验的可行性。     

基于多策略自适应差分进化算法的污水处理过程多目标优化控制

针对污水处理过程中的能耗过高和出水水质不达标等问题,提出一种基于多策略自适应差分进化算法的污水处理过程多目标优化控制方法。首先,在常规跟踪控制结构的基础上,增加对第3、4单元溶解氧浓度的跟踪控制,扩大了能耗和出水水质的优化调节范围。然后,设计一种多策略自适应差分进化算法（MSADE）,该算法采用多策略融合变异和排序优选方法,选取合适的变异策略和较优的随机个体引导种群变异,并根据进化过程信息自适应地更新交叉率,以提升算法的收敛性和pareto解的多样性。最后,将MSADE算法与PID控制器相结合,并构建以能耗和出水水质为优化目标的多目标优化问题,实现对溶解氧和硝态氮浓度设定值的动态寻优和跟踪控制。基于国际基准仿真平台BSM1进行验证,结果表明所提的多目标优化控制方法能有效降低污水处理过程的能耗并提升出水水质。     

基于多目标粒子群算法的污水处理智能优化控制

为了满足污水处理过程出水水质排放达标的同时降低运行能耗,提出了一种基于多目标粒子群的污水处理多目标智能优化控制方法。首先,通过分析污水处理运行数据,建立了基于自适应回归核函数的污水处理能耗和出水水质模型;其次,设计出一种污水处理多目标优化方法,利用多目标粒子群优化算法同时对污水处理自适应能耗和出水水质模型进行优化,获得溶解氧和硝态氮浓度的优化设定值;最后,利用PID控制器对溶解氧和硝态氮浓度优化设定值进行跟踪控制,实现了污水处理过程的多目标优化控制。基于污水处理基准仿真平台BSM1的实验结果显示,该多目标优化控制方法不但能够保证出水水质达标,而且能有效降低污水处理过程的能耗。     

基于均匀分布NSGAII算法的污水处理多目标优化控制

针对污水处理过程控制中能耗过大、出水水质超标严重等问题，提出了一种基于均匀分布的NSGAII(non-dominated sorting genetic algorithm II based on uniform distribution, UDNSGAII)多目标优化智能控制系统。首先，该方法以污水处理能耗和出水水质作为优化目标，建立多目标优化模型。其次，为了获得溶解氧和硝态氮的优化设定值，提高Pareto解的性能，该算法将种群映射到目标函数对应的超平面, 并在该平面上进行聚类以增加解的多样性。此外，加入分布性判断模块和分布性加强模块提高解的分布性。最后，采用比例积分微分(proportional integral derivative, PID)控制器对溶解氧和硝态氮的优化设定值进行底层跟踪控制。为了验证该算法的有效性，采用国际基准的污水处理仿真平台(benchmark simulation model No.1, BSM1)来进行实验。结果显示，所提出的UDNSGAII多目标优化控制方法能够在满足出水水质达标的同时，有效地降低污水处理过程能耗。     

基于多目标粒子群算法的污水处理智能优化控制

为了满足污水处理过程出水水质排放达标的同时降低运行能耗,提出了一种基于多目标粒子群的污水处理多目标智能优化控制方法。首先,通过分析污水处理运行数据,建立了基于自适应回归核函数的污水处理能耗和出水水质模型;其次,设计出一种污水处理多目标优化方法,利用多目标粒子群优化算法同时对污水处理自适应能耗和出水水质模型进行优化,获得溶解氧和硝态氮浓度的优化设定值;最后,利用PID控制器对溶解氧和硝态氮浓度优化设定值进行跟踪控制,实现了污水处理过程的多目标优化控制。基于污水处理基准仿真平台BSM1的实验结果显示,该多目标优化控制方法不但能够保证出水水质达标,而且能有效降低污水处理过程的能耗。     

全局校正的可变容差法及其在化工过程在线优化中的应用

传统在线优化算法在寻优时未考虑全局最优点对寻优的指导作用,且没有对优化搜索区域进行约束,因此,具有一定的盲目性,很难保证最终能够搜索到全局最优点,且优化解难以在线应用。针对传统优化算法的局限性,本文提出了一种全局优化指导局部优化的两层优化方法——全局校正的可变容差法,以遗传算法作为全局优化算法,可变容差法作为局部优化算法,以全局优化解调整局部优化算法的寻优方向,保证在线优化向全局最优点方向前进,指出全局优化算法和局部优化算法分别具有不同的优化周期和约束区域。将全局校正的可变容差法在数值函数中验证并应用于乙炔加氢反应器的在线优化,结果表明,与传统在线优化算法相比,这种方法不但能够减少寻优时间,也提高了寻优的精确度和有效性。     

乡镇生活污水处理工程设计优化

主要以乡镇生活污水处理工程设计优化方式为重点进行阐述,从生活污水排水量设计、进水与出水的水质设计、好氧生物处理法的优化、人工湿地处理法的优化等方面进行深入探索与研究,其目的在于加强乡镇生活污水处理力度,为改善生活环境提供有利条件。     

纸浆浓度自动化控制系统设计

研究在传统PID控制系统上提出一种改进SAA的优化算法对参数进行优化,并设计了基于改进SAA算法的纸浆浓度自动化控制系统。根据纸浆浓度自动化控制系统功能需求,对系统整体架构进行设计,并构建了纸浆浓度模型;针对传统PID无法在线调整参数的问题,设计了基于改进SAA算法的参数优化控制器;通过仿真实验对研究提出的算法和系统进行了验证。结果表明:提出的基于改进SAA算法相较于标准SAA算法,具有更好的寻优能力,表现出更好的稳定性与收敛性,且搜索效率更高;相较于传统PID和SAA-PID控制系统,产量提高0.65 t/h,成本下降4.37元/t,可用于实际纸浆浓度自动化控制。     

前馈神经网络与遗传算法相结合解决曲轴中心缩孔

引言缩痕与孔洞是塑件成型时一种常见的缺陷。当制件外层材料冷却固化后,心部材料开始冷却,心部收缩把制件表层拉向制件内部,引起缩痕,如果制件表层的刚度足够大,则将在制件内部产生孔洞而不是缩痕[1]。关于塑件成型过程中的缩痕、翘曲、收缩等缺陷问题,许多学者提出了工艺参数设计的单目标和多目标优化模型,如Kriging模型、BP神经网络、响应面法、支持向量回归等,针对这些模型,采用的优化求解算法主要有:遗传算法、粒子群法、蚁群算法等。申长雨等[2]采用神经网络与混合遗传算法结合优化注塑成型工艺,改善了制品的体收缩     

Synthesis and optimization of utility system using parameter adaptive differential evolution algorithm☆

Synthesis and optimization of utility system usual y involve grassroots design, retrofitting and operation optimi-zation, which should be considered in modeling process. This paper presents a general method for synthesis and optimization of a utility system. In this method, superstructure based mathematical model is established, in which different modeling methods are chosen based on the application. A binary code based parameter adaptive differential evolution algorithm is used to obtain the optimal configuration and operation conditions of the sys-tem. The evolution algorithm and models are interactively used in the calculation, which ensures the feasibility of configuration and improves computational efficiency. The capability and effectiveness of the proposed approach are demonstrated by three typical case studies.     

控制与工艺集成优化设计研究进展

控制与工艺集成优化设计是一种系统化的过程优化设计方法。以典型二元精馏塔设计为例阐述了其过程系统的数学描述，给出其最优工艺设计和控制设计的求解框架和求解策略，以及其中混合整数动态优化问题(MIDO)的求解方法。通过与传统的工艺与控制分步序贯设计比较，讨论了二者各自的优势和不足，指出控制与工艺集成优化设计具有良好的发展前景。     

Optimization of a Process Synthesis Superstructure Using an Ant Colony Algorithm

The optimization of chemical syntheses based on superstructure modeling is a perfect way for achieving the optimal plant design. However, the combinatorial optimization problem arising from this method is very difficult to solve, particularly for the entire plant. Relevant literature has focused on the use of mathematical programming approaches. Some research has also been conducted based on meta‐heuristic algorithms. In this paper, two approaches are presented to optimize process synthesis superstructure. Firstly, mathematical formulation of a superstructure model is presented. Then, an ant colony algorithm is proposed for solving this nonlinear combinatorial problem. In order to ensure that all the constraints are satisfied, an adaptive, feasible bound for each variable is defined to limit the search space. Adaptation of these bounds is executed by the suggested bound updating rule. Finally, the capability of the proposed algorithm is compared with the conventional Branch and Bound method by a case study.     

基于分解算法的功热交换网络多目标优化

功热网络设计问题指在流程设计中对变压和换热过程进行耦合优化设计的问题,以此来提高整体系统的能效并降低成本。前人工作中一般采用数学规划法对功热网络建模优化。然而,由于存在变压过程和换热器面积计算的非线性约束,以及换热匹配的二元变量,整体模型往往是一个高度非凸的混合整数非线性规划模型,难以求解。本文提出一种高效的功热网络优化方法。模型中分别用透平压缩机和换热器实现功热网络中轴功和热的交换。求解过程采用分解算法,主问题中用随机算法对关键变量优化,功和热两个子网络问题中用确定性算法求解。目标函数考虑了经济和环境影响。案例测试对比了不同优化目标得到的结果以及多目标Pareto曲线,验证了所提出方法的高效性。     

Analysis of the particle swarm algorithm in the optimization of a three-phase slurry catalytic reactor

The Particle Swarm Optimization (PSO) method was employed to optimize an industrial chemical process characterized by being difficult to be optimized by conventional deterministic methods. The chemical process is a three phase catalytic slurry reactor (tubular geometry) in which the reaction of the hydrogenation of o-cresol producing 2-methyl-cyclohexanol is carried out. The optimization problem was formulated considering as input variables the operating conditions of the reactor and as objective function the maximization of productivity, subject to the environmental constraint of conversion. The process was represented by a multivariable non-linear rigorous mathematical model and in order to solve the optimization problem, the performance of the PSO algorithm was evaluated considering four sets of parameters values suggested by the literature. PSO demonstrated to be efficient and robust to solve the constrained optimization problem, independently of the values of the PSO parameters. The solution of the rigorous mathematical model of the reactor was associated with a high computational burden, and although the PSO algorithm presented high rate of convergence, the attempt to make possible the optimization in a timeframe suitable to real time applications failed because the algorithm lost robustness (fraction of the number of runs the algorithm reached the optimization goal) when run with a reduced number of function evaluations. Therefore, if this type of application is desired, simplified mathematical models with fast and simple numerical methods must be preferred.     

气田地面集输管网参数优化设计

气田集输管网参数优选是气田地面工程规划设计的关键,开展集输管网参数优化可以有效降低集气系统投资。针对气田常用管网结构和工艺流程,考虑伴热带传热对于管道工艺计算的影响,建立了以管网建设费用最小为目标,以温压平衡、经济流速、节点流量平衡等为约束条件的气田集输管网参数优化数学模型,根据模型的结构特点设计了粒子群智能求解算法,结合C#语言和Access数据库开发了优化设计软件。实例验证得出,模型及算法正确,粒子群求解算法可靠性和收敛性良好,优化软件稳定高效。     

Deep learning-based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design

Identifying optimal photobioreactor configurations and process operating conditions is critical to industrialize microalgae-derived biorenewables. Traditionally, this was addressed by testing numerous design scenarios from integrated physical models coupling computational fluid dynamics and kinetic modeling. However, this approach presents computational intractability and numerical instabilities when simulating large-scale systems, causing time-intensive computing efforts and infeasibility in mathematical optimization. Therefore, we propose an innovative data-driven surrogate modeling framework, which considerably reduces computing time from months to days by exploiting state-of-the-art deep learning technology. The framework built upon a few simulated results from the physical model to learn the sophisticated hydrodynamic and biochemical kinetic mechanisms; then adopts a hybrid stochastic optimization algorithm to explore untested processes and find optimal solutions. Through verification, this framework was demonstrated to have comparable accuracy to the physical model. Moreover, multi-objective optimization was incorporated to generate a Pareto-frontier for decision-making, advancing its applications in complex biosystems modeling and optimization. © 2018 American Institute of Chemical Engineers AIChE J, 65: 915–923, 2019     

DMOS优化软件及其在化工过程优化中的应用

DMOS软件综合运用了模式识别、支持向量机、人工神经网络、遗传算法、线性和非线性回归等多种数据挖掘技术,能有效解决复杂工业过程系统优化中普遍存在的多因子、高噪声、非线性、非高斯分布和非均匀分布的难题。将DMOS工业优化软件成功地应用于柴油加氢精制装置及丙烯腈反应装置的生产优化。根据装置DCS系统采集的生产数据,研究了装置优化操作的主要工艺参数,采用模式识别方法建立了装置生产优化操作的定性模型,并最终建立了优化目标的数学模型。     

Rx-COSMO-CAMPD: Enhancing Reactions by Integrated Computer-Aided Design of Solvents and Processes based on Quantum Chemistry

Solvents strongly affect reaction-based chemical processes. Process design, therefore, needs to integrate solvent design. For this purpose, the integrated computer-aided molecular and process design (CAMPD) method Rx-COSMO-CAMPD is proposed. It employs a hybrid optimization scheme combining a genetic algorithm to explore the molecular design space with gradient-based optimization of the process. To overcome limitations of molecular design based on group-contribution methods, reaction kinetics and thermodynamic properties are predicted using advanced quantum-chemical methods. Rx-COSMO-CAMPD is demonstrated in a case study of a carbamate-cleavage process where promising solvents are designed efficiently. The results show that the integrated solvent and process design with Rx-COSMO-CAMPD outperforms computer-aided molecular design without process optimization in the identification of solvents that enable optimal process performance.