Multipurpose Batch Chemical Plants Research Articles

Cleaner manufacture of multipurpose batch chemical and biochemical plants. Scheduling and optimal choice of production recipes

This study presents an approach for cleaner management of multipurpose batch chemical or biochemical plants. Given that manufacture of most of the products could be carried out in multiple ways depending on chosen production recipes, the scheduling framework is extended by involving the choice of appropriate production recipes. Using as optimization criteria the Global or Local Environmental Impact Assessments defined in Minimum Environmental Impact Methodology, the mathematical formulation that results in multi-objective optimization problems is presented for a special class of schedules. The approach is implemented on an example from dairy industry. Production of two types of curds by selection from two available recipes for each one of them is considered. The problem is translated to a single objective. Two most reliable solutions are obtained, which differ in chosen production recipes, milkfat content and units involved for tasks implementation. Comments on the impacts on the load of Biochemical Oxygen Demand at the end of each production cycle due to simultaneous curds manufacturing and contributions of individual dairy wastes in Global Biochemical Oxygen Demand are also provided.

Addressing the scheduling of chemical supply chains under demand uncertainty

AbstractA multistage stochastic optimization model is presented to address the scheduling of supply chains with embedded multipurpose batch chemical plants under demand uncertainty. In order to overcome the numerical difficulties associated with the resulting large‐scale stochastic mixed‐integer‐linear‐programming (MILP) problem, an approximation strategy comprising two steps, and based on the resolution of a set of deterministic and two‐stage stochastic models is presented. The performance of the proposed strategy regarding computation time and optimality gap is studied through comparison with other traditional approaches that address optimization under uncertainty. Results indicate that the proposed strategy provides better solutions than stand‐alone two‐stage stochastic programming and two‐stage shrinking‐horizon algorithms for similar computational efforts and incurs much lower computation times than the rigorous multistage stochastic model. © 2006 American Institute of Chemical Engineers AIChE J, 2006

A computerized and integrated approach for heat exchanger network design in multipurpose batch plants

Significant savings in the utility cost of batch plants can be obtained by heat integration. In this study, an integrated mathematical programming approach is developed for the determination of the cost optimal heat exchanger network for multipurpose batch chemical plants. A single step, interactive computer program (BatcHEN) which is developed for the determination of the campaigns (i.e. the set of products which can be produced simultaneously), the heat exchange areas of all possible heat exchangers in the campaigns and finally the heat exchanger network are all discussed. A matrix search algorithm is used for the determination of the campaigns. Heat exchange areas for the possible heat exchangers are found by solving a nonlinear optimization model with a grid search algorithm. Finally the heat exchanger network optimization is modeled as a mixed integer linear programming problem and then solved by the modeling and optimization software GAMS/XA.

On-line fault diagnosis system support for reactive scheduling in multipurpose batch chemical plants

In this work, a simple strategy for the development and implementation of a fault diagnosis system (FDS) that interacts with a schedule optimiser in batch chemical plants is presented. The proposed FDS consists of an artificial neural network (ANN) structure supplemented with a knowledge-based expert system (KBES) in a block-oriented configuration. The system combines the adaptive learning diagnostic procedure of the ANN and the transparent deep knowledge representation of the KBES. The information needed to implement the FDS includes a historical database of past batches, a Hazard and Operability (HAZOP) analysis and a model of the plant. Two motivating case studies are presented to show the results of the proposed methodology. The first corresponds to a fed-batch reactor. In this example, the FDS performance is demonstrated through the simulation of different process faults. The second case study corresponds to a multipurpose batch plant. In this case, the results of reactive scheduling are shown by simulating different abnormal situations. A performance comparison is made against the traditional scheduling approach without the support of the proposed FDS.

Planejamento e programação da produção em plantas multipropósito operando em batelada na indústria química

Neste artigo, considera-se o problema de programação da produção a curto prazo em plantas químicas multipropósito operando em batelada, em que a produção da planta é determinada pela demanda a ser atendida. Nesta situação, a capacidade da planta não está bem definida porque depende do mix de produção. Propõe-se uma fase de planejamento que tem como objetivo a determinação do número de bateladas de cada tarefa necessárias para atender a produção, bem como a respectiva janela de tempo de processamento. Estas janelas permitem a análise do carregamento dos processadores e da factibilidade do plano, ou seja, o atendimento das datas de entrega, através de ferramentas desenvolvidas na área de Busca Orientada por Restrições. Esta análise é feita para uma atribuição fixa de tarefas a processadores introduzida pelo usuário. O sistema fornece informações para orientar o usuário na criação de diferentes cenários de atribuição. O resultado da fase de planejamento é formado por um conjunto de janelas de processamento que diminuem sensivelmente a dimensão do problema de programação da produção, como é discutido para duas abordagens: programação mista (Mixed Integer Linear Programming - MILP) e Simulated Annealing.

Short-term planning and scheduling in multipurpose batch chemical plants: a multi-level approach

This paper deals with short-term planning/scheduling problems when the product demands are driven by customer orders. This type of problem is, in general difficult to be solved, once the orders are subject to changes in short periods. In this paper is proposed a multi-level decomposition procedure, containing at least two levels. At the planning level, demands are adjusted, a raw material delivery plan is defined and a capacity analysis is performed. Therefore, time windows for each operation are defined. At the scheduling level, an MILP model is developed. In order to reduce scheduling problem dimension, it is performed an analysis of equipment contention profile, enabling to model only situations where a decision variable is requested.

Design of multipurpose batch chemical plants using a genetic algorithm

In this paper, we introduce the development and implementation of a Genetic Algorithm for the preliminary design of a multipurpose chemical batch plant. A two-stage approach combining a Discrete Event Simulation Model (D.E.S. model) and a Genetic Algorithm (G.A.) for optimal design is proposed. The goal is to minimise equipment investment cost for a fixed product slate and production level.

Scheduling multi-purpose batch plants with junction constraints

SUMMARY There is an increasing trend in the chemical process industry to operate flexible batch plants because of their capability to manufacture multiple products simultaneously by sharing the same process resources. In this paper, the scheduling of multi-purpose batch chemical plants with junction (header) constraints is considered. A mixed-integer non-linear model for the scheduling of multi-purpose batch chemical plants is formulated that considers the connection between equipment sets, transfer times, variable batch sizes, alternative process plans, and batch merging. Because of the computational time complexity of the batch scheduling problem, a heuristic scheduling algorithm that minimizes the total tardiness is developed lo solve the model.

Sequencing intermediate products: A practical solution for multipurpose production scheduling

In the present work, the production scheduling problem in multipurpose batch chemical plants operating under mixed intermediate storage policies is submitted to a new practical review leading to simplified and realistic solutions. Customer satisfaction is set as the main problem objective instead of minimization of well-known performance criteria such as makespan or flowtime and deviation of resulting production profile from desired deliveries is minimised using penalty functions including costs for stock and delay. The need to synchronise requirements and availability of the precise amounts of intermediates and products to a timetable is achieved by identifying multipurpose schedules as sequences of integers related to production runs of stable intermediates. Such a dramatic simplification of the problem allows the satisfactory use of stochastic optimization techniques (SA) for the matching of deliveries and due dates.

A combined scheduling/reactive scheduling strategy to minimize the effect of process operations uncertainty in batch plants

This paper deals with production scheduling of multipurpose batch chemical plants under uncertainty. The uncertainty associated with process operations causes that production can not be carried out exactly as scheduled. Variations in task processing times and equipment availability can make the initial schedule inefficient. A combined strategy has been proposed to minimize effects due to these variations. It involves the generation of robust schedules and then the application of a reactive scheduling procedure to adapt the original schedule to the new scenario. An industrial based case study is used to show the advantages of this combined strategy.

Study on optimization of operation for multipurpose batch chemical plants with multiple production routes

Study on optimization of operation for multipurpose batch chemical plants with multiple production routes

Study on optimization of operation for multipurpose batch chemical plants with multiple production routes

According to the operation characteristics of such plants, on the basis of Mauderli—Wellons's research, the mathematical models of operation optimization are modified which consider the campaign formation and production plan problem respectively. A new heuristics is advanced to search for the dominant production campaigns. Comparing the heuristics with Mauderli's algorithm, it ensures that parallel items in—phase have the same batch and that each production line operates with optimized batch, therefore the plant has a larger processing rate. This heuristics can effectively relieve the bottlenecks, and avoid the complexity resulting from the nonconvexity of the model. Within the resource alocated, the mixed—integer algorithm is used to maximize the profit.

Reactive schedule modification in multipurpose batch chemical plants

A new scheme is described for reactive schedule modification in the face of unexpected deviations in processing times and unit availabilities of a multipurpose batch plant (MBP). Schedule modification is done using at least impact heuristic beam search which proceeds in two levels: creation of a decision tree which makes use of possible reroutings of the product that is causing a conflict, and heuristic pruning of the search space to contain the combinatorial complexity. The heuristic chooses a path among all possible reroutings for a product such that the impact of each decision on the rest of the schedule is kept as small as possible. This approach has been implemented and tested on a number of simulated deviations in a MBP case study with three products. The proposed least impact heuristic was found to perform better than the earliest finishing unit heuristic in all the cases considered.

Scheduling of multipurpose batch chemical plants with resource constraints

The objective is to develop an efficient scheduling procedure for multipurpose batch chemical plants with resource constraints. The scheduling problem subject to resource restrictions is formulated as a mixed integer nonlinear programming (MINLP) model which is characterized by high combinatorial complexity and nonconvexity. These features are the main obstacles to the direct application of conventional optimization methods for the solution of the proposed model. Consequently, a two-level decomposition strategy is introduced which significantly mitigates the computational requirements associated with the solution of the MINLP model. The effectiveness of the proposed model and solution strategy is illustrated with a number of test examples

A new approach for determination the horizon constraints for design problem of multipurpose batch chemical plants

Referring to the multiperiod design model of multipurpose batch chemical plants, a novel approach for determination of the equivalent horizon constraints based on graph theory, and in particular, on obtaining a family of maximum independent vertex sets, is considered. A suitable method to find a family of maximum independent vertex sets, is proposed.

A new approach for determination the horizon constraints for design problem of multipurpose batch chemical plants

A new approach for determination the horizon constraints for design problem of multipurpose batch chemical plants

Scheduling of multipurpose batch chemical plants. 1. Formation of single-product campaigns

The single-product campaign formation problem is defined and a mixed integer nonlinear programming (MINLP) formulation is presented. The difficulties encountered in solving this formulation are discussed, a decomposition strategy to solve the problem is presented, and the effectiveness of the solution procedure is illustrated with an example

Scheduling of multipurpose batch chemical plants. 2. Multiple-product campaign formation and production planning

The single-product campaign formation decomposition algorithm, which is presented in part I, is extended to identify dominant multiple-product campaign based on the same linear dominance property used by Mauderli and Rippin. A production planning formulation for multipurpose batch plants is presented that selects from the set of previous by identified dominant campaigns and accounts for changeover and startup times of the production lines in each campaign. The dominant campaigns are then identified for the example multipurpose plant from Mauderli and Rippin, and several production planning problems are solved to illustrate the improvement in plant performance using the optimal dominant campaigns compared with the campaigns identified by Mauderli and Rippin

Scheduling for multipurpose batch chemical plants

The present paper is concerned with planning problems in multipurpose batch chemical plants. Concepts and solution proposals are presented for rough and fine planning with the help of Gantt chart drawing. The aim is to simulate and assess the consequences of decisions and to establish proposals for solution and aspects of implementation as well as to establish real orders for work. The consideration of specified restrictions for the model and their realization on a low cost microcomputer is described. In long-term and short-term planning of multipurpose plants for the production of fine chemicals, complicated scheduling problems occur. It is shown that even for complicated models the computer aided establishment of Gantt charts using a low- cost micro-computer is possible.

Optimal design of multi-purpose batch chemical plants

A new solution method has been developed for the optimal design of multi-purpose batch chemical plants. The minimum capital investment cost serves for the optimality criterion. The algorithm is built upon two decomposition stages: a combinatorial step allowing the use of some heuristics and a nonlinear programming stage. A quasi-optimal solution is guarantied.