Synthesis of refrigeration system based on generalized disjunctive programming model

Refrigeration system holds an important role in process industries. The optimal synthesis cannot only reduce the energy consumption, but also save the production costs. In this study, a general methodology is developed for the optimal design of refrigeration cycle and heat exchanger network (HEN) simultaneously. Taking the heat integration between the external heat sources/sinks and the refrigeration cycle into consideration, a superstructure with sub-coolers is developed. Through defining logical variables that indicate the relative temperature positions of refrigerant streams after sub-coolers, the synthesis is formulated as a Generalized Disjunctive Programming (GDP) problem based on LP transshipment model, with the target of minimizing the total compressor shaft work in the refrigeration system. The GDP model is then reformulated as a Mixed Integer Nonlinear Programming (MINLP) problem with the aid of binary variables and Big-M Constraint Method. The efficacy of the process synthesis model is demonstrated by a case study of ethylene refrigeration system. The result shows that the optimization can significantly reduce the exergy loss as well as the total compression shaft work.     

Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach

This article presents an integrated, simulation‐based optimization procedure that can determine the optimal process conditions for injection molding without user intervention. The idea is to use a nonlinear statistical regression technique and design of computer experiments to establish an adaptive surrogate model with short turn‐around time and adequate accuracy for substituting time‐consuming computer simulations during system‐level optimization. A special surrogate model based on the Gaussian process (GP) approach, which has not been employed previously for injection molding optimization, is introduced. GP is capable of giving both a prediction and an estimate of the confidence (variance) for the prediction simultaneously, thus providing direction as to where additional training samples could be added to improve the surrogate model. While the surrogate model is being established, a hybrid genetic algorithm is employed to evaluate the model to search for the global optimal solutions in a concurrent fashion. The examples presented in this article show that the proposed adaptive optimization procedure helps engineers determine the optimal process conditions more efficiently and effectively. POLYM. ENG. SCI., 47:684–694, 2007. © 2007 Society of Plastics Engineers.     

Injection Molding Warpage Optimization Based on a Mode-Pursuing Sampling Method

This paper presents a new optimization approach for minimizing the warpage defect of injection-molded plastic parts. Existing methods in warpage optimization are either computationally expensive or, when inexpensive surrogate models are employed with fixed set of sample points, the accuracy of the surrogate model will only be ensured by a large number of sample points, which in turn will increase the amount of required computation. To address this problem, this paper applies a mode-pursuing sampling (MPS) method for warpage optimization, by integrating injection molding simulation with MPS, and by proposing a reinforced convergence criterion for the optimization process, in an attempt to search for the optimal process parameters of injection molding for minimizing warpage defect both effectively and efficiently. The MPS method can systematically generate more sample points in the neighborhood of the current optimal solution while statistically covering the entire search space. A case study of a scanner frame, where injection time, melt temperature and mold temperature are selected as the design variables, demonstrates that the proposed optimization method can effectively decrease the warpage deflection of an injection-molded part with significantly less computation required. Based on the optimization results, the paper also studied the influences of different process parameters on the severity of the warpage defect, providing a guideline for the setting of the proper process parameters.     

Using surrogate models for efficient optimization of simulated moving bed chromatography

A new approach of using computationally cheap surrogate models for efficient optimization of simulated moving bed (SMB) chromatography is presented. Two different types of surrogate models are developed to replace the detailed but expensive full-order SMB model for optimization purposes. The first type of surrogate is built through a coarse spatial discretization of the first-principles process model. The second one falls into the category of reduced-order modeling. The proper orthogonal decomposition (POD) method is employed to derive cost-efficient reduced-order models (ROMs) for the SMB process. The trust-region optimization framework is proposed to implement an efficient and reliable management of both types of surrogates. The framework restricts the amount of optimization performed with one surrogate and provides an adaptive model update mechanism during the course of optimization. The convergence to an optimum of the original optimization problem can be guaranteed with the help of this model management method. The potential of the new surrogate-based solution algorithm is evaluated by examining a separation problem characterized by nonlinear bi-Langmuir adsorption isotherms. By addressing the feed throughput maximization problem, the performance of each surrogate is compared to that of the standard full-order model based approach in terms of solution accuracy, CPU time and number of iterations. The quantitative results prove that the proposed scheme not only converges to the optimum obtained with the full-order system, but also provides significant computational advantages.     

Data-driven tools for the optimization of a pharmaceutical process through its knowledge-driven model

The use of computationally demanding knowledge-driven models to optimize a process might encounter substantial numerical challenges. Because a model is an abstraction and approximation of the process, calculating the exact model optimum might not be necessary because its industrial implementation is bound to be an approximate one. Here we are exploring an alternative optimization route through a surrogate model. Because one of the decision variables affecting the optimization is time-varying, the Design of Dynamic Experiments is used to estimate the surrogate model. The process considered here is a freeze-drying process widely used in the pharmaceutical industry. The model used is a stochastic model describing the process in great detail. It is shown that the proposed data-driven route calculates the optimum in about 8 h, as opposed to 22 h for the knowledge-driven model, while sacrificing only <15% in the computed value of the process performance.     

Data-Driven Process Simulation Using Connected Surrogate Unit Models Exemplified on a Steam Methane Reforming Process

Process simulation based on physical models often faces computational problems with respect to convergence, especially if the underlying flowsheets are complex. The use of data-driven surrogate models connected to flowsheets promises to overcome these challenges. Using the steam methane reforming process, this paper presents the development of surrogate models – artificial neural networks – for the key units of the process that are subsequently connected to form the entire flowsheet. The accuracy of the individual surrogate models is analyzed based on the test error; the accuracy of the flowsheet is evaluated by a benchmark process simulation performed in Aspen Plus®. Therefore, the predicted key variables, here outlet temperatures and compositions, are compared to the benchmark. It is shown that their maximum error is below the typical measurement error. The comparison of the accuracy of the surrogate-based flowsheet simulation with the Aspen Plus® simulation proves to match very well, as long as the training ranges of the underlying surrogate models are not violated. The promising results of this paper pave the way for future work, such as the optimization of process parameters or superstructure optimization.     

Synergistic optimization framework for the process synthesis and design of biorefineries

The conceptual process design of novel bioprocesses in biorefinery setups is an important task,which remains yet challenging due to several limitations.We propose a novel framework incorporating superstructure optimization and simulation-based optimization synergistically.In this context,several approaches for superstructure optimization based on different surrogate models can be deployed.By means of a case study,the framework is introduced and validated,and the different superstructure optimization approaches are benchmarked.The results indicate that even though surrogate-based optimization approaches alleviate the underlying computational issues,there remains a potential issue regarding their validation.The development of appropriate surrogate models,comprising the selection of surrogate type,sampling type,and size for training and cross-validation sets,are essential factors.Regarding this aspect,satisfactory validation metrics do not ensure a successful outcome from its embedded use in an optimization problem.Furthermore,the framework’s synergistic effects by sequentially performing superstructure optimization to determine candidate process topologies and simulationbased optimization to consolidate the process design under uncertainty offer an alternative and promising approach.These findings invite for a critical assessment of surrogatebased optimization approaches and point out the necessity of benchmarking to ensure consistency and quality of optimized solutions.     

Demand-based optimization of a chlorobenzene process with high-fidelity and surrogate reactor models under trust region strategies

This work demonstrates the optimization of the industrial scale chlorobenzene process, which continuously produces multiple products and includes a multiphase reaction with bubble column reactors (BCRs). The trust region filter (TRF) method is applied to carry out the demand-based optimization of large chlorobenzene process with high-fidelity BCR models. The TRF method uses surrogate models that substitute the high-fidelity BCR models in the process model, and avoids the direct implementation of high-fidelity models, which leads to a large and intractable optimization problem. The surrogate models are constructed based on basis functions that apply first order corrections from the gradients of high-fidelity models. Different basis functions, CSTR and linear models, are studied in this work. As a result, the usage of CSTR models for the basis function leads to fewer function evaluations of the high-fidelity model because CSTR model is a reasonable approximation of the high-fidelity models and an initial guess of the optimization problem. Also, the TRF with surrogate models successfully provides an optimal solution of the high-fidelity process model with few iterations and function evaluations of the high-fidelity model itself. From the comparison with a low-fidelity CSTR model, the solution with the TRF presents more accurate results. The surrogate approaches also make a smooth transition from low- to high-fidelity models in process development. We apply this approach to a demand-based optimization that integrates nontrivial business options, including optimal shortage of customer demands for profitable operation.     

丙烯制冷系统改造及优化操作

本文简单介绍了丙烯制冷原理及包头煤化工分公司丙烯制冷系统工艺流程,着重讨论了对丙烯制冷系统原设计的改造,开车及运行过程中出现的问题、注意事项及整改措施。经过不断地技术改造、操作优化和经验总结,取得了明显成效,在安全稳定运行的基础上,不但节约大量丙烯,而且还缩短了开车时间。丙烯平均月耗从优化前的24t减少到现在的1t,开车时间也从4h缩短至2h,为公司节约了生产成本,也为以后的安全稳定运行奠定良好基础。     

A shortcut procedure for refrigeration systems

A shortcut model is presented for vapor-compression refrigeration cycles which is expressed explicitly in terms of temperature and requires only saturated refrigerant data for evaluation. Based on this model, an index of performance for pure refrigeration is proposed. This index can be used to quickly identify thermodynamically efficient refrigerants, and to predict the performance of refrigeration cycles with very little computational effort. Examples are presented to illustrate the quality of the approximations, as well as the usefulness of the proposed model for preliminary design calculations.     

中央空调制冷系统运行优化模型及实例分析

中央空调制冷系统能耗在建筑能耗中占有很大比重,而由于设计不合理、运行控制不合理等诸多因素的存在,导致中央空调制冷系统的能耗偏大,不符合我国可持续发展的原则。因此有必要研究建立中央空调制冷系统研究运行优化模型并在此基础上找到中央空调制冷系统优化控制策略。本文通过建立中央空调制冷系统运行优化的数学模型,并结合实际工程案例,计算出中央空调系统优化控制策略,以期为中央空调制冷系统优化运行提供参考。     

乙烯装置制冷系统模拟及优化

以化工流程模拟软件ASPEN PLUS为应用平台,建立了能良好描述裂解气在冷箱中预冷、在脱甲烷塔中分离和制冷系统工艺模型。应用该模型对扬子乙烯装置老区制冷系统进行了流程模拟、参数灵敏度分析和过程优化;研究了甲烷和乙烯冷剂分配、相同和不同温度级乙烯冷剂分配对乙烯损失的影响,以及相应操作参数的优化调整;找到了现有制冷系统的用能瓶颈;解决了工艺操作参数的优化问题;实现了装置高负荷工况下的经济运行。     

基于高斯模型的聚乙烯过程设计与控制集成优化

聚乙烯反应过程中物流-能流剧烈交叠、反应-传递相互耦合,使得过程具有强非线性以及多重稳态。传统的顺序设计方法不能保证系统有足够的控制自由度,当存在扰动和过程参数不确定性时,仅依靠设计控制器很难提高产品质量。提出一种聚乙烯工艺稳态设计与运行控制的集成优化方案,创造性地引入Kriging高斯模型同时预测模型动态和模型不确定性。另一个重要的贡献是在聚乙烯工艺设计阶段,设计性能指标,定量描述过程稳态设计对闭环动态的影响。所提出的方法已经通过对气相聚乙烯工艺设计和运行控制的集成优化进行了验证,并在参数不确定性和扰动存在情况下仿真证实了集成优化设计方案的高效性。     

合成氨装置中制冷方式的选择与优化

分析了合成氨装置中制冷工艺的原理、流程和特点;比较了氨吸收制冷和压缩制冷方式的技术经济指标;提出了降低制冷装置能耗的工艺优化方案和设计改进措施。     

基于EGO算法的常压塔能量优化

常减压装置能量消耗约占炼厂总用能的25%～30%,在保证产品产量与质量的条件下,优化常减压蒸馏塔操作条件,可有效降低能耗.为了避免随机优化算法对常压塔机理模型进行操作优化时,存在计算资源消耗大、效率低的问题,文中采用基于代理模型的全局优化方法优化常压塔的余热回收过程,在优化迭代过程中用Kriging代理模型来代替耗时的精确模型评估.实验表明模型调用次数相较于粒子群优化算法减少了90%,优化时间减少了85%,实现了能量优化并且保证了侧线产品之间的分离精度.     

An algorithm to determine sample sizes for optimization with artificial neural networks

This article presents an algorithm developed to determine the appropriate sample size for constructing accurate artificial neural networks as surrogate models in optimization problems. In the algorithm, two model evaluation methods—cross‐validation and/or bootstrapping—are used to estimate the performance of various networks constructed with different sample sizes. The optimization of a CO₂ capture process with aqueous amines is used as the case study to illustrate the application of the algorithm. The output of the algorithm—the network constructed using the appropriate sample size—is used in a process synthesis optimization problem to test its accuracy. The results show that the model evaluation methods are successful in identifying the general trends of the underlying model and that objective function value of the optimum solution calculated using the surrogate model is within 1% of the actual value. © 2012 American Institute of Chemical Engineers AIChE J, 59: 805–812, 2013     

基于自适应采样算法的芳烃异构化代理模型

异构化是芳烃生产中的重要环节，提高异构化环节的建模和优化效率对工业生产有着重要意义。但是，直接使用机理模型的优化过程耗时较长，优化效率低。代理模型可以有效地对机理模型进行近似，而代理模型采样方法对模型精度有很大影响。提出了一种新的基于稀疏度和最邻近期望的自适应采样算法，该方法可以平衡全局搜索和局部搜索，通过求解优化问题找到反映函数关键信息的新采样点，再加入原始样本集中，使得代理模型精度不断提高。多个测试函数结果表明，相比于其他自适应采样算法，该算法能有效提升代理模型精度和建模效率。该算法在芳烃异构化环节代理模型中也得到了有效验证，与本文中其他算法对比，该算法模型误差减少5%以上，建模时间缩短30%以上。     

Multi-objective optimization of injection molding parameters,based on the Gkriging-NSGA-vague method

Plastic production quality, manufacturing cost, and molding efficiency are three important indices for a new product development. In addition to injection molding process parameters (IMPP), runner system also has an important role in the injection molding process. In this study, the plastic production quality, manufacturing costs, and molding efficiency are considered as the optimized objectives. The design parameters include runner diameters and IMPP. The improved Kriging surrogate model (Gkriging), nondominated sorting genetic algorithm (NSGA-II), and multicriteria fuzzy decision-making approach (vague sets) are combined, and the Gkriging-NSGA-vague scheme is proposed to optimize the runner diameters and the IMPP. Firstly, the Gkriging model is established to map the correlation between design parameters and optimized objectives. Based on the Gkriging model, the NSGA-II is combined with predictive models to obtain the Pareto-optimal solutions. Then, the optimal Pareto-optimal solution is obtained by the vague approach. A multicavity mold with two different plastic parts is utilized as the design case. The optimization results indicate that the Gkriging-NSGA-vague method is a powerful method for solving the multi-objective optimization problems. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 137, 48659.     

基于bootstrap GEI算法的碳二加氢反应器代理模型

碳二加氢反应器是乙烯工厂的一个重要装置,其运行状况直接影响着乙烯产品的纯度和产量,因此,提高碳二加氢反应过程的建模和优化效率对于工业实际具有重要意义。但是,直接对碳二加氢反应器的CFD高精度分析模型进行优化,计算量大,优化效率非常低。本文从模型预测方差准确性和提高全局搜索的有效性出发,基于Kriging代理模型,提出了一种求取无偏预测方差的广义期望提高算法--bootstrap GEI算法,通过测试函数的仿真对比,与bootstrap EI算法相比,该算法能够从全局角度搜索最优样本点,减少样本点的个数,从而提高模型更新和优化效率。该算法在实际工业碳二加氢等温反应器的代理模型中也得到了有效验证。     

Integrated ionic liquid and rate-based absorption process design for gas separation: Global optimization using hybrid models

A new method for integrated ionic liquid (IL) and absorption process design is proposed where a rigorous rate-based process model is used to incorporate absorption thermodynamics and kinetics. Different types of models including group contribution models and thermodynamic models are employed to predict the process-relevant physical, kinetic, and thermodynamic (gas solubility) properties of ILs. Combining the property models with process models, the integrated IL and process design problem is formulated as an MINLP optimization problem. Unfortunately, due to the model complexity, the problem is prone to convergence failure. To lower the computational difficulty, tractable surrogate models are used to replace the complex thermodynamic models while maintaining the prediction accuracy. This provides an opportunity to find the global optimum for the integrated design problem. A pre-combustion carbon capture case study is provided to demonstrate the applicability of the method. The obtained global optimum saves 14.8% cost compared with the Selexol process.