Novel genetic algorithm for short-term scheduling of sequence dependent changeovers in multiproduct polymer plants

Polymer plants generally operate to produce different grades of product from the same reactor. Such systems commonly require short-term scheduling to meet market demand. One important requirement in continuous-time scheduling of such systems is to satisfy a variety of constraints, including identifying feasible sequences of the predecessor and successor jobs to effectively handle changeovers. In this study, a new genetic algorithm (GA) is proposed to solve such job sequencing problems. The proposed GA uses real-coded chromosome to represent job orders and their sequences in the schedule. The novelty is that the representation ensures that all constraints are satisfied a priori, except the sequence constraint which is handled by penalizing violations. Three important problems relevant to polymer industry are solved to obtain optimal schedules. The first deals with the sequencing constraint between individual product orders, the second with sequencing constraint between groups of product orders, while the third incorporates batching with scheduling.     

关于三个水厂净水工艺与供水水质的比较和分析

该文介绍了国内一自来水公司三个水厂的两个原水水质、净水工艺及出厂水水质。结果显示第一水厂的出厂水质较为理想,第二水厂次之,第三水厂为第三。第三水厂由于水源的问题导致出厂氨氮季节性超标,建议采取有效措施改进水源水质,以提高出厂水质。第二水厂需进行工艺改造,实施臭氧活性炭深度处理以进一步提高供水水质。第三水厂一期系统臭氧生物活性炭池置于砂滤池后较二期活性炭滤池置于砂滤池前出水有机物CODMn及TOC略低,但两者基本相近。建议第三水厂采取必要的措施改进水源水质,或再增加一道臭氧生物活性炭工序。     

Optimal control and operation of drum granulation processes

Process optimisation and optimal control of batch and continuous drum granulation processes are studied in this paper. The main focus of the current research has been: (i) construction of optimisation and control relevant, population balance models through the incorporation of moisture content, drum rotation rate and bed depth into the coalescence kernels; (ii) investigation of optimal operational conditions using constrained optimisation techniques; (iii) development of optimal control algorithms based on discretized population balance equations; and (iv) comprehensive simulation studies on optimal control of both batch and continuous granulation processes. The objective of steady state optimisation is to minimise the recycle rate with minimum cost for continuous processes. It has been identified that the drum rotation-rate, bed depth (material charge), and moisture content of solids are practical decision (design) parameters for system optimisation. The objective for the optimal control of batch granulation processes is to maximize the mass of product-sized particles with minimum time and binder consumption. The objective for the optimal control of the continuous process is to drive the process from one steady state to another in a minimum time with minimum binder consumption, which is also known as the state-driving problem. It has been known for some time that the binder spray-rate is the most effective control (manipulative) variable. Although other possible manipulative variables, such as feed flow-rate and additional powder flow-rate have been investigated in the complete research project, only the single input problem with the binder spray rate as the manipulative variable is addressed in the paper to demonstrate the methodology. It can be shown from simulation results that the proposed models are suitable for control and optimisation studies, and the optimisation algorithms connected with either steady state or dynamic models are successful for the determination of optimal operational conditions and dynamic trajectories with good convergence properties.     

密炼中心计算机条形码综合信息网

介绍了密炼中心计算机条形码综合信息网的基本组成、使用效果和技术保密等级。该网由原材料层、配料层、混炼现场层、混炼控制层、成品管理层、密炼中心管理层和成品检验层组成 ,具有橡胶密炼过程的智能控制、物料管理和成品质量追溯功能     

A mixed integer programming approach for scheduling commodities in a pipeline

This paper addresses the problem of developing an optimisation structure to aid the operational decision-making of scheduling activities in a real-world pipeline scenario. The pipeline connects an inland refinery to a harbour, conveying different types of oil derivatives. The optimisation structure is developed based on mixed integer linear programming (MILP) with uniform time discretisation, but the MILP well-known computational burden is avoided by the proposed decomposition strategy, which relies on an auxiliary routine to determine temporal constraints, two MILP models, and a database. The scheduling of operational activities takes into account product availability, tankage constraints, pumping sequencing, flow rate determination, and a variety of operational requirements. The optimisation structure main task is to predict the pipeline operation during a limited scheduling horizon, providing low cost operational procedures. Illustrative instances demonstrate that the optimisation structure is able to define new operational points to the pipeline system, providing significant cost saving.     

Multi-week MILP scheduling for an ice cream processing facility

This paper presents a multi-week mixed integer linear programming (MILP) scheduling model for an ice cream processing facility. The ice cream processing is a typical complex food manufacturing process and a simplified version of this processing has been adapted to investigate scheduling problems in the literature. Most of these models only considered the production scheduling for a week. In this paper, multi-week production scheduling is considered. The problem has been implemented as an MILP model. The model has been tested on a set of cases from the literature, and its results were compared to the results of problems solved using hybrid MILP-heuristics methods in the literature. The inclusion of clean-up session, weekend break and semi-processed product from previous week were also assessed with two additional sets of experiments. The experiments result show that the proposed MILP is able to handle multi-week scheduling efficiently and effectively within a reasonable time limit.     

Simultaneous regular and non-regular production scheduling of multipurpose batch plants: A real chemical–pharmaceutical case study

Regular and non-regular production can often be found in multipurpose batch plants, requiring two distinct operating strategies: campaign and short-term production. This paper proposes a solution approach for simultaneous scheduling of campaign and short-term products in multipurpose batch plants. Regular products follow a cyclic schedule and must cover several product deliveries during the scheduling horizon, while non-regular products have a non-cyclic schedule. The proposed approach explores the Resource-Task Network (RTN) discrete-time formulation. Moreover, a rolling horizon approach, and reformulation and branching strategies have been applied to deal with the computational complexity of the scheduling problem. Real case instances of a chemical–pharmaceutical industry are solved, showing the applicability of the solution approach.     

Flexible retrofit design of multiproduct batch plants

We treat the addition of new equipment to an existing multiproduct batch plant for which new production targets and selling profits have been specified. This optimal retrofit design problem has been considered by Vaselenak et al. (Ind. Engng Chem. Res. 26, 718–728, 1987). Their constraint that new units must be used in the same manner for all products places a restriction on the design which could readily be overcome in practice. We present a mixed-integer nonlinear programming (MINLP) formulation which eliminates this constraint. A series of examples is presented which demonstrate greater profitability for plants designed with our formulation. The examples also bring to fight a further unwanted constraint in the Vaselenak, Grossmann and Westerberg formulation. In their formulation they limit batch size to the smallest unit at a stage, even when that unit is not needed. It is noted that, at the expense of some additional mathematical complexity, our formulation could be enhanced to allow reconnexion of existing units and alternate use of multiple additional units.     

A novel network-based continuous-time representation for process scheduling: Part II. General framework

In the first part of this series of papers we presented a new network-based continuous-time representation for the short-term scheduling of batch processes, which overcomes numerous shortcomings of existing approaches. In this second part, we discuss how this representation can be extended to address aspects such as: (i) preventive maintenance activities on unary resources (e.g., processing and storage units) that were planned ahead of time; (ii) resource-constrained changeover activities on processing and shared storage units; (iii) non-instantaneous resource-constrained material transfer activities; (iv) intermediate deliveries of raw materials and shipments of finished products at predefined times; and (v) scenarios where part of the schedule is fixed because it has been programmed in the previous scheduling horizon. The proposed integrated framework can be used to address a wide variety of process scheduling problems, many of which are intractable with existing tools.     

A decision tree based decomposition method for oil refinery scheduling

Refinery scheduling attracts increasing concerns in both academic and industrial communities in recent years. However, due to the complexity of refinery processes, little has been reported for success use in real world refineries. In academic studies, refinery scheduling is usually treated as an integrated, large-scale optimization problem, though such complex optimization problems are extremely difficult to solve. In this paper, we proposed a way to exploit the prior knowledge existing in refineries, and developed a decision making system to guide the scheduling process. For a real world fuel oil oriented refinery, ten adjusting process scales are predetermined. A C4.5 decision tree works based on the finished oil demand plan to classify the corresponding category (i.e. adjusting scale). Then, a specific sub-scheduling problem with respect to the determined adjusting scale is solved. The proposed strategy is demonstrated with a scheduling case originated from a real world refinery.     

Optimal energy consumption in refrigeration systems ‐ modelling and non‐convex optimisation

Supermarket refrigeration consumes substantial amounts of energy. However, due to the thermal capacity of the refrigerated goods, parts of the cooling capacity delivered can be shifted in time without deteriorating the food quality. In this study, we develop a realistic model for the energy consumption in super market refrigeration systems. This model is used in a Nonlinear Model Predictive Controller (NMPC) to minimise the energy used by operation of a supermarket refrigeration system. The model is non‐convex and we develop a computational efficient algorithm tailored to this problem that is somewhat more efficient than general purpose optimisation algorithms for NMPC and still near to optimal. Since the non‐convex cost function has multiple extrema, standard methods for optimisation cannot be directly applied. A qualitative analysis of the system's constraints is presented and a unique minimum within the feasible region is identified. Following that finding we propose a tailored minimisation procedure that utilises the nature of the feasible region such that the minimisation can be separated into two linear programs; one for each of the control variables. These subproblems are simple to solve but some iterations might have to be performed in order to comply with the maximum capacity constraint. Finally, a nonlinear solver is used for a small example without separating the optimisation problem, and the results are compared to the outcome of our proposed minimisation procedure for the same conceptual example. The tailored approach is somewhat faster than the general optimisation method and the solutions obtained are almost identical. © 2012 Canadian Society for Chemical Engineering     

Lead chemicals—compliance with environmental regulations

Early recognition of certain hazards in the manufacture and use of lead chemicals resulted in industry programs to monitor and protect the worker. Unlike many other chemicals where problems have been identified only recently, experience has provided a good base for meeting the new, more stringent regulations affecting lead chemicals. Current, scheduled, and proposed OSHA and EPA regulations will be summarized, and engineering, product physical form and packaging solutions will be discussed. Problems to be addressed will include handling of raw materials and finished products during lead stabilizer manufacture, shipment and handling and use in plastic compounding and processing plants. Options currently available, technologies under development, and factors affecting option selection will be reviewed. Long-term experience with lead chemicals supports the optimism regarding the feasibility of compliance.     

A novel constraint programming model for large-scale scheduling problems in multiproduct multistage batch plants: Limited resources and campaign-based operation

This contribution introduces an efficient constraint programming (CP) model that copes with large-scale scheduling problems in multiproduct multistage batch plants. It addresses several features found in industrial environments, such as topology constraints, forbidden product-equipment assignments, sequence-dependent changeover tasks, dissimilar parallel units at each stage, limiting renewable resources and multiple-batch orders, among other relevant plant characteristics. Moreover, the contribution deals with various inter-stage storage and operational policies. In addition, multiple-batch orders can be handled by defining a campaign operating mode, and lower and upper bounds on the number of batches per campaign can be fixed. The proposed model has been extensively tested by means of several case studies having various problem sizes and characteristics. The results have shown that the model can efficiently solve medium and large-scale problems with multiple constraining features. The approach has also rendered good quality solutions for problems that consider multiple-batch orders under a campaign-based operational policy.     

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.     

A THRESHOLD-ACCEPTING METAHEURISTIC METHOD FOR SCHEDULING THE OPERATIONS OF DEHYDRATION PLANTS

ABSTRACT

Dehydration plants are broadly characterized by a multi-product nature chiefly attributed to the utilization of different raw materials to be processed sequentially so that demand constraints are met. Processing of raw materials is implemented through a series of preprocessing operations that together with drying constitute the production procedure of a pre-specified programme. The core of the manufacturing system that a typical dehydration plant involves, is scheduling of operations so that demand is fulfilled within a pre-determined time horizon imposed by production planning. The typical scheduling operation that dehydration plants involve can be formulated as a general job shop scheduling problem. The aim of this study is to describe a new metaheuristic method for solving the job shop scheduling problem of dehydration plants, termed as the Backtracking Adaptive Threshold Accepting (BATA) method. Our effort focuses on developing an innovative method, which produces reliable and high quality solutions, requiring reasonable computing effort. The main innovation of this method, towards a typical threshold accepting algorithm, is that during the optimization process the value of the threshold is not only lowered, but also raised or backtracked according to how effective a local search is. BATA is described in detail while a characteristic job shop scheduling case study for dehydration plant operations is presented.     

Vortex motion‐based particle swarm optimisation for energy consumption of alumina evaporation

The aim of the alumina evaporation process is to improve the concentration of sodium aluminate solution by evaporating the excess water contained in the solution. The evaporation is achieved using heat from steam. Since steam consumption is the major operating costs, in this paper, we investigate an operation optimisation problem for the evaporation process to minimise steam consumption subject to a constraint on the particular quality of the final sodium aluminate solution. This paper proposes a new particle swarm optimisation (PSO) algorithm based on vortex motion to solve this optimisation problem. We demonstrate the effectiveness of the PSO algorithm on benchmark functions. We then apply it to a real industrial evaporation process, where the optimal results show that the steam consumption is considerably reduced. © 2012 Canadian Society for Chemical Engineering     

Valid Inequalities Based on Demand Propagation for Chemical Production Scheduling MIP Models

Although several mixed‐integer programming (MIP) models have been proposed for the scheduling of chemical manufacturing facilities, the development of solution methods for these formulations has received limited attention. In this article, we develop a constraint propagation algorithm for the calculation of lower bounds on the number and size of tasks necessary to satisfy given demand. These bounds are then used to express four types of valid inequalities which greatly enhance the computational performance of the MIP scheduling model. Specifically, the addition of these inequalities leads to reductions in the computational requirements of more than three orders of magnitude, thereby allowing us to address medium‐sized problems of industrial relevance. Importantly, the proposed methods are applicable to a wide range of problem classes and time‐indexed MIP models for chemical production scheduling. © 2013 American Institute of Chemical Engineers AIChE J, 59:872‐887, 2013     

Optimal control of batch processes using particle swam optimisation with stacked neural network models

An optimal control strategy for batch processes using particle swam optimisation (PSO) and stacked neural networks is presented in this paper. Stacked neural network models are developed form historical process operation data. Stacked neural networks are used to improve model generalisation capability, as well as provide model prediction confidence bounds. In order to improve the reliability of the calculated optimal control policy, an additional term is introduced in the optimisation objective function to penalize wide model prediction confidence bounds. The optimisation problem is solved using PSO, which can cope with multiple local minima and could generally find the global minimum. Application to a simulated fed-batch process demonstrates that the proposed technique is very effective.     

用于多产品间歇化工过程排序的模拟退火算法

提出了一种适用于多产品间歇化工过程排序的改进模拟退火算法 ,该算法采用了非平衡、多次退火策略及多个相邻解产生方法 .通过对无限中间储罐 (UIS)类问题的研究表明 ,同已有的方法相比 ,该方法求解精度高 ,计算速度快 ,适合于求解多产品间歇化工过程的排序问题 .     

Demand response scheduling under uncertainty: Chance-constrained framework and application to an air separation unit

Recent increases in renewable power generation challenge the operation of the power grid: generation rates fluctuate in time and are not synchronized with power demand fluctuations. Demand response (DR) consists of adjusting user electricity demand to match available power supply. Chemical plants are appealing candidates for DR programs; they offer large, concentrated loads that can be modulated via production scheduling. Price-based DR is a common means of engaging industrial entities; its benefits increase significantly when a longer (typically, a few days) scheduling time horizon is considered. DR production scheduling comes with its own challenges, related to uncertainty in future (i.e., forecast) electricity prices and product demand. In this work, we provide a framework for DR production scheduling under uncertainty based on a chance-constrained formulation that also accounts for the dynamics of the production facility. The ideas are illustrated with an air separation unit case study.