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Synergistic optimization framework for the process synthesis and design of biorefineries

《化学科学与工程前沿（英文）》 2022年第16卷第2期页码 251-273 doi: 10.1007/s11705-021-2071-9

摘要： 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 simulation-based optimization to consolidate the process design under uncertainty offer an alternative and promising approach. These findings invite for a critical assessment of surrogate-based optimization approaches and point out the necessity of benchmarking to ensure consistency and quality of optimized solutions.

关键词： biotechnology surrogate modelling superstructure optimization simulation-based optimization process design

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Split-order consolidation optimization for online supermarkets: Process analysis and optimization models

《工程管理前沿（英文）》页码 499-516 doi: 10.1007/s42524-022-0221-5

摘要： The large-scale online supermarket is a newly emerging online retailing mode which brings great convenience to people. Online supermarkets are characterized by having large amounts of daily orders with potentially multiple items, diverse delivery times, and a high order-split rate. Multiple shipments for one order caused by order splitting result in high cost and disturbance and a large number of discarded consumable packages at online retailers and customers, causing severe damage to the environment. Accordingly, research on split-order consolidation fulfilment is critical for the advancement of the practice and theory in the context of highly complex online retailing. This paper first analyzes the characteristics and the challenges associated with the split-order consolidation problem that online supermarket is confronting and summarizes the new operational process of split-order consolidation fulfilment. Then, a time–space network optimization model is built, and its corresponding solution algorithm is presented to solve the questions of where and when to consolidate the split orders. Finally, the computation results of the numerical experiments are provided to verify the effectiveness of the algorithm, and a sensitivity analysis of the relevant parameters is performed. This work highlights the effect of order consolidation processes and fulfilment methods on the order fulfilment decision-making for online supermarkets. The purpose of this article is to help pave the way for more effective online supermarket management and order implementation.

关键词： online supermarkets split-order consolidation time–space network genetic algorithm

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Optimization of multi-objective integrated process planning and scheduling problem using a priority basedoptimization algorithm

Muhammad Farhan AUSAF,Liang GAO,Xinyu LI

《机械工程前沿（英文）》 2015年第10卷第4期页码 392-404 doi: 10.1007/s11465-015-0353-y

摘要：

For increasing the overall performance of modern manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling (IPPS) problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS (MOIPPS) problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

关键词： integrated process planning and scheduling (IPPS) dispatching rules priority based optimization algorithm multi-objective optimization

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Optimization of remanufacturing process routes oriented toward eco-efficiency

Hong PENG, Han WANG, Daojia CHEN

《机械工程前沿（英文）》 2019年第14卷第4期页码 422-433 doi: 10.1007/s11465-019-0552-z

摘要： Remanufacturing route optimization is crucial in remanufacturing production because it exerts a considerable impact on the eco-efficiency (i.e., the best link between economic and environmental benefits) of remanufacturing. Therefore, an optimization model for remanufacturing process routes oriented toward eco-efficiency is proposed. In this model, fault tree analysis is used to extract the characteristic factors of used products. The ICAM definition method is utilized to design alternative remanufacturing process routes for the used products. Afterward, an eco-efficiency objective function model is established, and simulated annealing (SA) particle swarm optimization (PSO) is applied to select the manufacturing process route with the best eco-efficiency. The proposed model is then applied to the remanufacturing of a used helical cylindrical gear, and optimization of the remanufacturing process route is realized by MATLAB programming. The proposed model’s feasibility is verified by comparing the model’s performance with that of standard SA and PSO.

关键词： remanufacturing process route optimization eco-efficiency simulated particle swarm optimization algorithm IDEF0

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Perspectives in multilevel decision-making in the process industry

Braulio BRUNAUD, Ignacio E. GROSSMANN

《工程管理前沿（英文）》 2017年第4卷第3期页码 256-270 doi: 10.15302/J-FEM-2017049

摘要： Decisions in supply chains are hierarchically organized. Strategic decisions involve the long-term planning of the structure of the supply chain network. Tactical decisions are mid-term plans to allocate the production and distribution of materials, while operational decisions are related to the daily planning of the execution of manufacturing operations. These planning processes are conducted independently with minimal exchange of information between them. Achieving a better coordination between these processes allows companies to capture benefits that are currently out of their reach and improve the communication among their functional areas. We propose a network representation for the multilevel decision structure and analyze the components that are involved in finding integrated solutions that maximize the sum of the benefits of all nodes of the decision network. Although such task is very challenging, significant research progress has been made in each component of this structure. An overview of strategic models, mid-term planning models, and scheduling models is presented to address the solution of each node in the decision network. Coordination mechanisms for converging the integrated solutions are also analyzed, including solving large-scale models, multiobjective optimization, bi-level programming, and decomposition. We conclude by summarizing the challenges that hinder the full integration of multilevel decision making in supply chain management.

关键词： supply chain optimization enterprise-wide optimization multilevel optimization planning scheduling

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Design and optimization of reactive distillation: a review

《化学科学与工程前沿（英文）》 2022年第16卷第6期页码 799-818 doi: 10.1007/s11705-021-2128-9

摘要： Reactive distillation process, a representative process intensification technology, has been widely applied in the chemical industry. However, due to the strong interaction between reaction and separation, the extension of reactive distillation technology is restricted by the difficulties in process analysis and design. To overcome this problem, the design and optimization of reactive distillation have been widely studied and illustrated for plenty of reactive mixtures over the past three decades. These design and optimization methods of the reactive distillation process are classified into three categories: graphical, optimization-based, and evolutionary/heuristic methods. The primary objective of this article is to provide an up-to-date review of the existing design and optimization methods. Desired and output information, advantages and limitations of each method are stated, the modification and development for original methodologies are also reviewed. Perspectives on future research on the design and optimization of reactive distillation method are proposed for further research.

关键词： reactive distillation process intensification design method reactive phase diagram optimization algorithm

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Multiscale process systems engineering—analysis and design of chemical and energy systems from moleculardesign up to process optimization

《化学科学与工程前沿（英文）》 2022年第16卷第2期页码 137-140 doi: 10.1007/s11705-021-2135-x

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Optimization and simultaneous heat integration design of a coal-based ethylene glycol refining process

《化学科学与工程前沿（英文）》 2023年第17卷第9期页码 1280-1288 doi: 10.1007/s11705-023-2301-4

摘要： Coal to ethylene glycol still lacks algorithm optimization achievements for distillation sequencing due to high-dimension and strong nonconvexity characteristics, although there are numerous reports on horizontal comparisons and process revamping. This scenario triggers the navigation in this paper into the simultaneous optimization of parameters and heat integration of the coal to ethylene glycol distillation scheme and double-effect superstructure by the self-adapting dynamic differential evolution algorithm. To mitigate the influence of the strong nonconvexity, a redistribution strategy is adopted that forcibly expands the population search domain by exerting external influence and then shrinks it again to judge the global optimal solution. After two redistributive operations under the parallel framework, the total annual cost and CO₂ emissions are 0.61%/1.85% better for the optimized process and 3.74%/14.84% better for the superstructure than the sequential optimization. However, the thermodynamic efficiency of sequential optimization is 11.63% and 10.34% higher than that of simultaneous optimization. This study discloses the unexpected great energy-saving potential for the coal to ethylene glycol process that has long been unknown, as well as the strong ability of the self-adapting dynamic differential evolution algorithm to optimize processes described by the high-dimensional mathematical model.

关键词： ethylene glycol redistribution heat integration optimization parallel framework

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Decision support for the development, simulation and optimization of dynamic process models

Norbert Asprion, Roger Böttcher, Jan Schwientek, Johannes Höller, Patrick Schwartz, Charlie Vanaret, Michael Bortz

《化学科学与工程前沿（英文）》 2022年第16卷第2期页码 210-220 doi: 10.1007/s11705-021-2046-x

摘要： Simulation is besides experimentation the major method for designing, analyzing and optimizing chemical processes. The ability of simulations to reflect real process behavior strongly depends on model quality. Validation and adaption of process models are usually based on available plant data. Using such a model in various simulation and optimization studies can support the process designer in his task. Beneath steady state models there is also a growing demand for dynamic models either to adapt faster to changing conditions or to reflect batch operation. In this contribution challenges of extending an existing decision support framework for steady state models to dynamic models will be discussed and the resulting opportunities will be demonstrated for distillation and reactor examples.

关键词： decision support multicriteria optimization model validation dynamic model sensitivity analysis

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Hybrid method integrating machine learning and particle swarm optimization for smart chemical process

Haoqin Fang, Jianzhao Zhou, Zhenyu Wang, Ziqi Qiu, Yihua Sun, Yue Lin, Ke Chen, Xiantai Zhou, Ming Pan

《化学科学与工程前沿（英文）》 2022年第16卷第2期页码 274-287 doi: 10.1007/s11705-021-2043-0

摘要： Modeling and optimization is crucial to smart chemical process operations. However, a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations, chemical reactions and separations. This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity. Thus, this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties. An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method. Firstly, a data set was generated based on process mechanistic simulation validated by industrial data, which provides sufficient and reasonable samples for model training and testing. Secondly, four well-known machine learning methods, namely, K-nearest neighbors, decision tree, support vector machine, and artificial neural network, were compared and used to obtain the prediction models of the processes operation. All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features. Finally, optimal process operations were obtained by using the particle swarm optimization approach.

关键词： smart chemical process operations data generation hybrid method machine learning particle swarm optimization

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Tool path strategy and cutting process monitoring in intelligent machining

Ming CHEN, Chengdong WANG, Qinglong AN, Weiwei MING

《机械工程前沿（英文）》 2018年第13卷第2期页码 232-242 doi: 10.1007/s11465-018-0469-y

摘要：

Intelligent machining is a current focus in advanced manufacturing technology, and is characterized by high accuracy and efficiency. A central technology of intelligent machining—the cutting process online monitoring and optimization—is urgently needed for mass production. In this research, the cutting process online monitoring and optimization in jet engine impeller machining, cranio-maxillofacial surgery, and hydraulic servo valve deburring are introduced as examples of intelligent machining. Results show that intelligent tool path optimization and cutting process online monitoring are efficient techniques for improving the efficiency, quality, and reliability of machining.

关键词： intelligent machining tool path strategy process optimization online monitoring

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Optimization of process parameters for mature landfill leachate pretreatment using MAP precipitation

Wei LI, Xiaowen DING, Min LIU, Yuewen GUO, Lei LIU

《环境科学与工程前沿（英文）》 2012年第6卷第6期页码 892-900 doi: 10.1007/s11783-012-0440-9

摘要： Chemical precipitation is a useful technology as a pretreatment to treat mature landfill leachate with high concentrations of ammonium-nitrogen ( ) and refractory organic compounds. Orthogonal experiments and factorial experiments were carried out to determine the optimal conditions enhancing the magnesium ammonium phosphate (MAP) precipitation process, and the experimental results demonstrated that the removal rate of was more than 85% when MgO and NaH PO ·2H O were applied as external sources of magnesium and phosphorous under the optimal conditions that molar ratio n(Mg)∶n(N)∶n(P) = 1.4∶1∶0.8, reaction time 60 min, original pH of leachate and settling time 30 min. In the precipitation process, pH could be maintained at the optimal range of 8–9.5 because MgO could release hydroxide ions to consume hydrogen ions. Calcium ions and carbonate ions existed in the leachate could affect the precipitation process, which resulted in the decrease of removal efficiency. The residues of MAP sediments decomposed by heating under alkaline conditions can be reused as the sources of phosphorous and magnesium for the removal of high concentrations of , and up to 90% of ammonium could be released under molar ratio of n[OH]∶n[MAP] = 2.5∶1, heating temperature 90°C and heating time 2h.

关键词： magnesium ammonium phosphate precipitation mature landfill leachate optimization ammonium-nitrogen

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Intelligent methods for the process parameter determination of plastic injection molding

Huang GAO, Yun ZHANG, Xundao ZHOU, Dequn LI

《机械工程前沿（英文）》 2018年第13卷第1期页码 85-95 doi: 10.1007/s11465-018-0491-0

摘要：

Injection molding is one of the most widely used material processing methods in producing plastic products with complex geometries and high precision. The determination of process parameters is important in obtaining qualified products and maintaining product quality. This article reviews the recent studies and developments of the intelligent methods applied in the process parameter determination of injection molding. These intelligent methods are classified into three categories: Case-based reasoning methods, expert system-based methods, and data fitting and optimization methods. A framework of process parameter determination is proposed after comprehensive discussions. Finally, the conclusions and future research topics are discussed.

关键词： injection molding intelligent methods process parameters optimization

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Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on

Ravindra Nath YADAV, Vinod YADAVA, G.K. SINGH

《机械工程前沿（英文）》 2013年第8卷第3期页码 319-332 doi: 10.1007/s11465-013-0269-3

摘要：

The effective study of hybrid machining processes (HMPs), in terms of modeling and optimization has always been a challenge to the researchers. The combined approach of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) has attracted attention of researchers for modeling and optimization of the complex machining processes. In this paper, a hybrid machining process of Electrical Discharge Face Grinding (EDFG) and Diamond Face Grinding (DFG) named as Electrical Discharge Diamond face Grinding (EDDFG) have been studied using a hybrid methodology of ANN-NSGA-II. In this study, ANN has been used for modeling while NSGA-II is used to optimize the control parameters of the EDDFG process. For observations of input-output relations, the experiments were conducted on a self developed face grinding setup, which is attached with the ram of EDM machine. During experimentation, the wheel speed, pulse current, pulse on-time and duty factor are taken as input parameters while output parameters are material removal rate (MRR) and average surface roughness (R_a). The results have shown that the developed ANN model is capable to predict the output responses within the acceptable limit for a given set of input parameters. It has also been found that hybrid approach of ANN-NSGA-II gives a set of optimal solutions for getting appropriate value of outputs with multiple objectives.

关键词： hybrid machining processes (HMPs) electrical discharge diamond grinding (EDDG) artificial neural network (ANN) genetic algorithm modeling and optimization

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Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzingelectro chemical machining process

Reza TEIMOURI, Hamed SOHRABPOOR

《机械工程前沿（英文）》 2013年第8卷第4期页码 429-442 doi: 10.1007/s11465-013-0277-3

摘要：

Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.

关键词： electrochemical machining process (ECM) modeling adaptive neuro-fuzzy inference system (ANFIS) optimization cuckoo optimization algorithm (COA)

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标题作者时间类型操作

Synergistic optimization framework for the process synthesis and design of biorefineries

期刊论文

Split-order consolidation optimization for online supermarkets: Process analysis and optimization models

期刊论文

Optimization of multi-objective integrated process planning and scheduling problem using a priority basedoptimization algorithm

Muhammad Farhan AUSAF,Liang GAO,Xinyu LI

期刊论文

Optimization of remanufacturing process routes oriented toward eco-efficiency

Hong PENG, Han WANG, Daojia CHEN

期刊论文

Perspectives in multilevel decision-making in the process industry

Braulio BRUNAUD, Ignacio E. GROSSMANN

期刊论文

Design and optimization of reactive distillation: a review

期刊论文

Multiscale process systems engineering—analysis and design of chemical and energy systems from moleculardesign up to process optimization

期刊论文

Optimization and simultaneous heat integration design of a coal-based ethylene glycol refining process

期刊论文

Decision support for the development, simulation and optimization of dynamic process models

Norbert Asprion, Roger Böttcher, Jan Schwientek, Johannes Höller, Patrick Schwartz, Charlie Vanaret, Michael Bortz

期刊论文

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process

Haoqin Fang, Jianzhao Zhou, Zhenyu Wang, Ziqi Qiu, Yihua Sun, Yue Lin, Ke Chen, Xiantai Zhou, Ming Pan

期刊论文

Tool path strategy and cutting process monitoring in intelligent machining

Ming CHEN, Chengdong WANG, Qinglong AN, Weiwei MING

期刊论文

Optimization of process parameters for mature landfill leachate pretreatment using MAP precipitation

Wei LI, Xiaowen DING, Min LIU, Yuewen GUO, Lei LIU

期刊论文

Intelligent methods for the process parameter determination of plastic injection molding

Huang GAO, Yun ZHANG, Xundao ZHOU, Dequn LI

期刊论文

Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on

Ravindra Nath YADAV, Vinod YADAVA, G.K. SINGH

期刊论文

Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzingelectro chemical machining process

Reza TEIMOURI, Hamed SOHRABPOOR

期刊论文

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