ENVIRONMENTALLY CONSCIOUS HOIST SCHEDULING FOR ELECTROPLATING FACILITIES

Hoist scheduling in electroplating operations has long been considered a key factor for improving the production rate. It has recently been recognized that hoist scheduling can also play an important role in waste minimization. In this work, a new hoist scheduling method is introduced for simultaneously achieving both the economic and environmental goals. A two-step dynamic optimization algorithm is introduced for identifying an optimal hoist schedule that can minimize the quantity and toxicity of wastewater streams from an electroplating line without loss of production rate. To improve computational efficiency, an engineering approach is adopted to reduce the number of binary decision variables in the optimization problem. An application to an actual electroplating process shows a significant reduction of both chemical and water consumption, which equates to a simultaneous realization of wastewater reduction and increase of profits.     

Simultaneous mixed-integer dynamic optimization for environmentally benign electroplating

Hoist scheduling, especially cyclic hoist scheduling (CHS), is used to maximize the manufacturing productivity of electroplating processes. Water-reuse network design (WRND) for the electroplating rinsing system targets the optimal water allocation, such that fresh water consumption and wastewater generation are minimized. Currently, there is still a lack of studies on integrating CHS and WRND technologies for electroplating manufacturing. In this paper, a multi-objective mixed-integer dynamic optimization (MIDO) model has been developed to integrate CHS and WRND technologies for simultaneous consideration of productivity and water use efficiency for environmentally benign electroplating. The orthogonal collocation method on finite elements is employed to convert the MIDO problem into a mixed-integer nonlinear programming (MINLP) problem. The efficacy of the methodology is demonstrated by solving a real electroplating example. It demonstrates that the computational methods of production scheduling, process design, and dynamic optimization can be effectively integrated to create economic and environmental win-win situations for the electroplating industry.     

Production-ratio oriented optimization for multi-recipe material handling via simultaneous hoist scheduling and production line arrangement

In multi-recipe and multi-stage material handling (M3H) processes, such as electroplating and polymeric coating, the productivity maximization under a customized production ratio of different types of jobs is an ultimate goal. Cyclic hoist scheduling (CHS) is certainly the most concerned aspect to improve the productivity. However, the production-ratio oriented productivity not only depends on the hoist scheduling, but also substantially relies on the production line arrangement (PLA), i.e., the spatial allocation of various processing units. This is because PLA determines the traveling time for the hoist to perform loaded and free moves among different processing units, which in turn inevitably affects the cyclic scheduling time and thus the total productivity. Therefore, CHS and PLA should be simultaneously optimized for the production-ratio oriented productivity maximization for M3H processes.In this paper, an integrated modeling methodology for productivity maximization of M3H processes has been developed with simultaneous consideration of CHS, PLA, and the customized production ratio. It introduces an MILP model that can successfully address all the major concerned issues for M3H processes, such as multiple recipes, multiple jobs, multi-capacity processing units, diverse processing time requirements, production line arrangement, and the customized production ratio in each cycle production. The efficacy of the proposed methodology is demonstrated by various case studies with in-depth analysis.     

Real‐time dynamic hoist scheduling for multistage material handling process under uncertainties

Multistage material handling processes are broadly used for manufacturing various products/jobs, where hoists are commonly used to transport inline products according to their processing recipes. When multiple types of jobs with different recipes are simultaneously and continuously handled in a production line, the hoist movement scheduling should be thoroughly investigated to ensure the operational feasibility of every job inline and in the meantime to maximize the productivity if possible. The hoist scheduling will be more complicated, if uncertainties of new coming jobs are considered, that is, the arrival time, type, recipe, and number of new jobs are totally unknown and unpredictable before they join the production line. To process the multiple jobs already inline and the newly added jobs, the hoist movements must be swiftly rescheduled and precisely implemented whenever new job(s) come. Because a reschedule has to be obtained online without violating processing time constraints for each job, the solution identification time for rescheduling must be taken into account by the new schedule itself. All these stringent requisites motivate the development of real‐time dynamic hoist scheduling (RDHS) targeting online generation of reschedules for productivity maximization under uncertainties. Hitherto, no systematic and rigorous methodologies have been reported for this study. In this article, a novel RDHS methodology has been developed, which takes into account uncertainties of new coming jobs and targets real‐time scheduling optimality and applicability. It generally includes a reinitialization algorithm to accomplish the seamless connection between the previous scheduling and rescheduling operations, and a mixed‐integer linear programming model to obtain the optimal hoist reschedule. The RDHS methodology addresses all the major scheduling issues of multistage material handling processes, such as multiple recipes, multiple jobs, multicapacity processing units, diverse processing time requirements, and even optimal processing queue for new coming jobs. The efficacy of the developed methodology is demonstrated through various case studies. © 2012 American Institute of Chemical Engineers AIChE J, 59: 465–482, 2013     

Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty

Scheduling of steelmaking-continuous casting (SCC) processes is of major importance in iron and steel operations since it is often a bottleneck in iron and steel production. In practice, uncertainties are unavoidable and include demand fluctuations, processing time uncertainty, and equipment malfunction. In the presence of these uncertainties, an optimal schedule generated using nominal parameter values may often be suboptimal or even become infeasible. In this paper, we introduce robust optimization and stochastic programming approaches for addressing demand uncertainty in steelmaking continuous casting operations. In the robust optimization framework, a deterministic robust counterpart optimization model is introduced to guarantee that the production schedule remains feasible for the varying demands. Also, a two-stage scenario based stochastic programming framework is investigated for the scheduling of steelmaking and continuous operations under demand uncertainty. To make the resulting stochastic programming problem computationally tractable, a scenario reduction method has been applied to reduce the number of scenarios to a small set of representative realizations. Results from both the robust optimization and stochastic programming methods demonstrate robustness under demand uncertainty and that the robust optimization-based solution is of comparable quality to the two-stage stochastic programming based solution.     

Rolling horizon based planning and scheduling integration with production capacity consideration

The rolling horizon method has been proposed to address the integrated production planning and scheduling optimization problem. Since the method can generally result in small-scale optimization model and fast solution, it has been used in a number of applications in realistic industrial planning and scheduling problems. In this paper, it is first pointed out that the incorporation of valid production capacity information into the planning model can improve the solution quality in the rolling horizon solution framework. A novel method is then proposed to derive the production capacity information representing the detail scheduling model based on parametric programming technique. A heuristic process network decomposition strategy is further applied to reduce the computational effort needed for larger and more complex process networks. Several case studies have been studied, which illustrate the efficiency of the proposed methodology in improving the solution quality of rolling horizon method for integrated planning and scheduling optimization.     

Integration of electroplating process design and operation for simultaneous productivity maximization,energy saving,and freshwater minimization

Electroplating industry annually produces numerous plated workpieces for U.S. pillar industries. However, it is also a major environment polluter in forms of wastewater, sludge, and spent solution. Thus, waste source reduction for the electroplating industry is a must. In practice, when productivity and energy use efficiency are also taken into account, the design and operation of an electroplating process become very complicated and need an in-depth study. In this paper, productivity maximization, energy saving, and freshwater/wastewater minimization are simultaneously addressed for the optimal design and operation of electroplating processes, which generates a triple-objective mixed-integer dynamic optimization (MIDO) model. The MIDO model is iteratively solved by a developed methodology to obtain the 3D Pareto frontier of the optimization problem, which provides important technical supports for the design and operation of electroplating processes. The efficacy has been demonstrated with a case study on an electroplating process.     

Short-term scheduling and recipe optimization of blending processes

The main objective of this paper is to develop an integrated approach to coordinate short-term scheduling of multi-product blending facilities with nonlinear recipe optimization. The proposed strategy is based on a hierarchical concept consisting of three business levels: Long-range planning, short-term scheduling and process control. Long-range planning is accomplished by solving a large-scale nonlinear recipe optimization problem (multi-blend problem). Resulting blending recipes and production volumes are provided as goals for the scheduling level. The scheduling problem is formulated as a mixed-integer linear program derived from a resource-task network representation. The scheduling model permits recipe changeovers in order to utilize an additional degree of freedom for optimization. By interpreting the solution of the scheduling problem, new constraints can be imposed on the previous multi-blend problem. Thus bottlenecks arising during scheduling are considered already on the topmost long-range planning level. Based on the outlined approach a commercial software system has been designed to optimize the operation of in-line blending and batch blending processes. The application of the strategy and software is demonstrated by a detailed case study.     

不确定条件下炼化企业计划与调度整合策略

A strategy for the integration of production planning and scheduling in refineries is proposed.This strategy relies on rolling horizon strategy and a two-level decomposition strategy.This strategy involves an upper level multiperiod mixed integer linear programming(MILP) model and a lower level simulation system,which is extended from our previous framework for short-term scheduling problems [Luo,C.P.,Rong,G.,"Hierarchical approach for short-term scheduling in refineries",Ind.Eng.Chem.Res.,46,3656-3668(2007)].The main purpose of this extended framework is to reduce the number of variables and the size of the optimization model and,to quickly find the optimal solution for the integrated planning/scheduling problem in refineries.Uncertainties are also considered in this article.An integrated robust optimization approach is introduced to cope with uncertain parameters with both continuous and discrete probability distribution.     

镀银生产线的控制

简要介绍镀银生产线的工作原理及主要功能，该生产线采用先进的设备及自控系统，具有多种自动控制功能，提高了高频元器件镀银质量和可靠性，适用于多种工艺要求的电镀生产控制，尤其是小批量，多品种的零件生产。     

Theoretical and computational comparison of continuous‐time process scheduling models for adjustable robust optimization

Coping with uncertainty in system parameters is a prominent hurdle when scheduling multi‐purpose batch plants. In this context, our previously introduced multi‐stage adjustable robust optimization (ARO) framework has been shown to obtain more profitable solutions, while maintaining the same level of immunity against risk, as compared to traditional robust optimization approaches. This paper investigates the amenability of existing deterministic continuous‐time scheduling models to serve as the basis of this ARO framework. A comprehensive computational study is conducted that compares the numerical tractability of various models across a suite of literature benchmark instances and a wide range of uncertainty sets. This study also provides, for the first time in the open literature, robust optimal solutions to process scheduling instances that involve uncertainty in production yields. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3055–3070, 2018     

电镀行业清洁生产审核技术要点的探讨

在解读电镀行业清洁生产相关标准、法规与要求的基础上,探讨了电镀行业开展清洁生产审核需规范的问题,推荐电镀行业污染防治方法及清洁生产减排效益的计算方法。指出规范计量管理是电镀行业实施清洁生产的基础,提出审核应重点关注的技术操作要点。对规范清洁生产审核工作,实现电镀行业生产全过程污染控制及重金属减排有指导意义。     

考虑需求不确定性的化工生产计划与调度集成

生产计划与调度是化工供应链优化中两个重要的决策问题。为了提高生产决策的效率,不仅要对计划与调度进行集成,而且要考虑不确定性的影响。对于多周期生产计划与调度问题,首先在每个生产周期内,分别建立计划与调度的确定性模型,通过产量关联对二者进行集成。然后考虑需求不确定性,使用有限数量的场景表达决策变量,建立二阶段随机规划模型。最后运用滚动时域求解策略,使计划与调度结果在迭代过程中达到一致。实例结果表明,在考虑需求不确定性时,与传统方法相比,随机规划方法可以降低总费用,结合计划与调度的分层集成策略,实现了生产操作性和经济性的综合优化。     

Simultaneous mixed-integer dynamic scheduling of processes and their energy systems

Increasingly volatile electricity prices make simultaneous scheduling optimization desirable for production processes and their energy systems. Simultaneous scheduling needs to account for both process dynamics and binary on/off-decisions in the energy system leading to challenging mixed-integer dynamic optimization problems. We propose an efficient scheduling formulation consisting of three parts: a linear scale-bridging model for the closed-loop process output dynamics, a data-driven model for the process energy demand, and a mixed-integer linear model for the energy system. Process dynamics is discretized by collocation yielding a mixed-integer linear programming (MILP) formulation. We apply the scheduling method to three case studies: a multiproduct reactor, a single-product reactor, and a single-product distillation column, demonstrating the applicability to multiple input multiple output processes. For the first two case studies, we can compare our approach to nonlinear optimization and capture 82% and 95% of the improvement. The MILP formulation achieves optimization runtimes sufficiently fast for real-time scheduling.     

Multi-objective modeling and optimization for scheduling of cracking furnace systems

Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usual y run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non-linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables (MDNSGA-I ) is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta-tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.     

高分子表面金属化技术

高分子表面金属化是对高分子材料表面进行改性处理使其具有良好物理性能、力学性能及金属光泽的表面技术,广泛应用于高分子导电、薄膜修饰、电磁屏蔽等领域。本文综述了高分子表面金属化的两大类技术方法,即干法镀膜和湿法镀膜。介绍了几种典型的表面处理技术,如真空镀膜、喷涂金属转移法、化学镀、化学还原金属化以及电镀,并总结了它们的技术特点以及在科学研究和工业生产应用中的难点。阐明了从传统电镀技术发展而来的高分子表面直接电镀工艺的优势,直接电镀工艺省去了电镀前的活化工序,缩短了工艺时间,避免了电镀前工艺对环境造成的污染,成为高分子表面金属化技术发展的新方向。     

分类分析法在预防和控制电镀故障中的应用

运用分类分析法对电镀故障进行统计分析,可以找出电镀故障发生的主要原因,制定出相应对策,以预防和控制故障,从而可以提高生产的可靠性与安全性。通过案例说明了电镀故障的收集、分类、编码、统计和分析方法。生产中应用该方法后,电镀故障明显下降。     

电镀行业清洁生产审核中的物料平衡

根据多年的清洁生产审核实践,总结了电镀、热浸镀、磷化、电泳、酸洗、阳极氧化等表面处理工艺的物料平衡图。以电镀锌为例,介绍了建立物料平衡的操作方法。以某厂的镀铬槽为例,通过实际计算,讲解了如何根据物料平衡分析来提出清洁生产方案。     

自行葫芦输送线在电泳涂装线的应用选型

重点介绍了自行葫芦输送系统的概念、功能、组成结构、工作流程、实际使用效果,其结构新颖、运行稳定、承载能力大,对于业界汽车涂装生产线自行葫芦输送系统的应用具有一定的参考意义。     

Boron-Doped Diamond Electrode Performance in Cr(VI) Reduction Using Synthetic and Plating Wastewater

The goal of this work was to evaluate the effectiveness of iron and boron-doped-diamond (BDD) as cathodic electrodes on the reduction of Cr(VI) in synthetic and wastewater samples. The pH and electrolyte composition were varied, and the effect on the Cr(VI) reduction rate was measured. The optimized conditions from the synthetic water Cr(VI) reduction experimental data, were tasted on electroplating wastewater. The results indicated that both a pH of 2 and the use of NaCl as an electrolyte significantly increase the Cr(VI) reduction rate for all synthetic systems, especially the iron-BDD system. The Cr(VI) reduction rate in Fe-BDD systems was also affected by nitrate and sulfate ions. In the case of electroplating wastewater, Cr(VI) reduction by BDD cathodes was faster than with iron cathodes, achieving a complete reduction of 180 mg Cr(VI)/L in 25 min, with 40% less sludge produced. The elemental composition of sludge was analyzed using SEM/EDS and X-ray spectroscopy to confirm that iron and chromium precipitated out of the solution. The sludge had a chemical composition of (31.9%) Fe₂O₃, (29.6%) FeOOH, (21%) FeO, and (17.4%) FeSO₄. Therefore, BDD as an electrode material effectively reduces Cr(VI) in electroplating wastewater, and can be effectively scaled up to industrial applications.