Batch Chemical Plants Research Articles

MULTICRITERIA OPTIMIZATION OF MULTIPRODUCT BATCH CHEMICAL PROCESS USING GENETIC ALGORITHM

ABSTRACT Optimal design problems are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is first formulated as a multiobjective optimization problem, to be solved using the well‐suited nondominating sorting genetic algorithm (NSGA‐II). The NSGA‐II has the capability to achieve fine tuning of variables in determining a set of nondominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA‐II ability to identify a set of optimal solutions provides the decision maker with a complete picture of the optimal solution space to gain better and appropriate choices. The effectiveness of NSGA‐II method with multiobjective optimization problem is illustrated through a carefully referenced example. PRACTICAL APPLICATIONSThe inherent dynamic nature of batch processes allows for their ability to handle variations in feedstock and product specifications, and provides the flexibility required for multiproduct or multipurpose facilities. They are thus best suited for the manufacture of low‐volume, high‐value products, such as specialty chemicals, pharmaceuticals, agricultural, food and consumer products, and most recently the constantly growing spectrum of biotechnology‐enabled products. Batch processing in the food industry has some unit operations, such as panning, whipping (emulsifying), and the pulling of taffy, which are not too often used in other industries, as well as other operations,such as blending, tabletting (including granulation) and lyophilization, which are common in other industries. Reduced time to market, lower production costs and improved flexibility are all critical success factors for batch processes.

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.

A PDDL+ Benchmark Problem: The Batch Chemical Plant

The PDDL+ language has been mainly devised to allow modelling of real-world systems, with continuous, time-dependant dynamics. Several interesting case studies with these characteristics have been also proposed, to test the language expressiveness and the capabilities of the support tools. However, most of these case studies have not been completely developed so far. In this paper we focus on the batch chemical plant case study, a very complex hybrid system with nonlinear dynamics that could represent a challenging benchmark problem for planning techniques and tools. We present a complete PDDL+ model for such system, and show an example application where the UPMurphi universal planner is used to generate a set of production policies for the plant.

Search for Optimal Design of Multiproduct Batch Plants under Uncertain Demand using Gaussian Process Modeling Solved by Heuristics Methods

This work deals with the problem of the search for optimal design of multiproduct batch chemical plants found in a chemical engineering process with uncertain demand. The aim of this work is to minimize the investment cost and find out the number and size of parallel equipment units in each stage. For this purpose, it is proposed to solve the problem in two different ways: the first way is by using Monte Carlo Method (MC) and the second way is by Genetics Algorithms (GAs). This GAs consider an effective mixed continuous discrete coding method with a four- point crossover operator, which take into account simultaneously, the uncertainty on the demand using Gaussian process modeling with two criteria maximization the Net Present Value (NPV) and Flexibility Index (FI). The results (number and size of equipment, investment cost, production time (Hi), NPV, FI, CPU time and Idle times in plant) obtained by GAs are better than the MC. This methodology can help the decision makers and constitutes a very promising framework for finding a set of "good solutions."

Revising the master production schedule in a HPP framework context

This paper investigates the stability of the master production schedule (MPS) in a multi-product batch chemical plant, a typical example of manufacturing plants in the process industry. The effects of demand pattern, replanning periodicity, setup costs and unit production cost on the performance of the MPS in a rolling horizon situation are examined. Adopting the hierarchical production planning framework, a two-level mathematical model is developed to conduct the study. A comprehensive simulation work and statistical analyses are reported in this work. The results of the study show that replanning periodicity significantly influences the scheduling instability and the impact of setup cost on scheduling instability is dependent on the demand pattern and the unit production cost. Moreover, the findings indicate that replanning frequency does not affect the total cost of the system if the cost structures are not extreme. Finally, the results show that cost structures affect the batching of orders (converting production quantities into an optimal number of batches to be processed).

Optimal Solutions of Multiproduct Batch Chemical Process Using Multiobjective Genetic Algorithm with Expert Decision System

Optimal design problem are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is firstly formulated as a multiobjective optimization problem, to be solved using the well suited non dominating sorting genetic algorithm (NSGA-II). The NSGA-II have capability to achieve fine tuning of variables in determining a set of non dominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA-II ability to identify a set of optimal solutions provides the decision-maker DM with a complete picture of the optimal solution space to gain better and appropriate choices. Then an outranking with PROMETHEE II helps the decision-maker to finalize the selection of a best compromise. The effectiveness of NSGA-II method with multiojective optimization problem is illustrated through two carefully referenced examples.

Bottom-up Modeling of the Steam Consumption in Multipurpose Chemical Batch Plants Focusing on Identification of the Optimization Potential

A detailed approach for modeling the steam consumption in a multipurpose chemical batch plant was developed, tested, and used for analysis of the energy-efficiency. The main advantage of the approach presented in this paper compared to available modeling approaches is the ability to describe the transient steam consumption. Thus, the new approach can be used for the dynamic optimization of batch operations with respect to the energy efficiency. The bottom-up method was implemented by modeling particular unit operations (UOs) in a case study plant, and validation was accomplished with direct measurements on both UO and building level. The principle of the bottom-up model is a detailed energy balance of each particular UO for which process parameter measurements are necessary as input data. These were extracted from the measurements archive of the case study plant for a period of two months. Process data from almost 1000 sensors installed in ca. 100 UOs were acquired, transformed into a time-series with common time basis, and used as an input data for the model. Special attention was paid to model the losses of the UOs because in earlier studies it was found that these are significant. Loss models were developed in the form of empirical parametric equations considering the losses due to radiation and the internal losses in the heating/cooling system due to inefficient operation. The parameters of the loss models were fitted, based on the developed methodology, to steam measurements of 4 UOs and consequently integrated into the overall bottom-up model for modeling other UOs as well. The energy usage efficiency of the UOs was inferred and the optimization spots were identified. The results in the case study plant have indicated that the energy savings potential for particular UOs with low steam-usage efficiency can be easily identified and serve as a good hint for the overall plant energy auditing and steam consumption optimization.

Integrated Operational Planning and Medium-Term Scheduling for Large-Scale Industrial Batch Plants

The operational planning and the medium-term scheduling of a multipurpose and multiproduct batch chemical plant are inter-related activities that involve the allocation of plant resources. Because of their disparate time scales, however, the effective integration of planning and scheduling has proven to be a formidable task. The lack of an integrative framework for planning and scheduling will invariably cause the planning model to provide unrealistic production targets to the scheduling level, leading to the misallocation of plant resources. In response to this issue, a novel framework for the integration of planning and scheduling for a multipurpose and multiproduct batch plant is presented. The framework entails integrating a novel planning with production disaggregation model with a medium-term scheduling model through a forward-rolling horizon approach.

Managing risk through a flexible recipe framework

AbstractA novel approach is proposed that exploits the use of a flexible recipe framework as a better way to handle the risk associated with the scheduling under uncertainty of batch chemical plants. The proposed solution strategy relies on a novel two‐stage stochastic formulation that explicitly includes the trade‐off between risk and profit at the decision‐making level. The model uses a continuous‐time domain representation and the generalized notion of precedence. Management of risk is explicitly addressed by including a control measure (i.e., the profit in the worst scenario), as an additional objective to be considered, thus, leading to a multiobjective optimization problem. To overcome the numerical difficulties associated with such mathematical formulation, a decomposition strategy based on the sample average approximation (SAA) is introduced. The main advantages of this approach are illustrated through a case study, in which a set of solutions appealing to decision makers with different attitudes toward risk are obtained. The potential benefits of the proposed flexible recipe framework as a way of managing the risk associated with the plant operation under demand uncertainty are highlighted through comparison with the conventional approach that considers nominal operating conditions. Numerical results corroborate the advantages of exploiting the capabilities of the proposed flexible recipe framework for risk management purposes. © 2008 American Institute of Chemical Engineers AIChE J, 2008

Synthesis of Parallel Operation for Enhanced Chemical Plant Operation

There are many ways to enhance the operation of chemical batch plants. One way is to use a parallel operation where two or more operation tasks are executed in parallel or overlap in a time period. This requires providing a computer-aided operation design environment with the facility to design and validate parallel operation. In this research paper, the different aspects of managing parallel operation are analyzed, and enhancements are proposed and implemented within the automated operation management solution CAPE-Oper. Implementation and system design considerations are discussed using a case study of a chemical batch plant.

Metaheuristic multiobjective optimisation approach for the scheduling of multiproduct batch chemical plants

This article introduces a novel framework to deal with the scheduling problem concerning batch chemical plants. The main contribution of this work in comparison with previous approaches relies on the fact that it includes the environmental impact as an objective to be optimised, integrated with other more commonly used as makespan and/or financial performance. To address this problem, an advanced planning and scheduling tool (APS), which includes an environmental evaluation module based on the Life Cycle Assessment (LCA) criteria, a temporisation algorithm and a financial module, coupled with a set of multiobjective genetic algorithms allows the computation of a set of non-dominated solutions in terms of the predefined criteria. This set of solutions can be used by the decision-maker in order to select the one that represents the right compromise among the different objectives.

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

Optimal design of multiproduct batch chemical plant using NSGA‐II

AbstractThe optimal design of a multiproduct batch chemical plant is formulated as a multiobjective optimization problem, and the resulting constrained mixed‐integer nonlinear program (MINLP) is solved by the nondominated sorting genetic algorithm approach (NSGA‐II). By putting bounds on the objective function values, the constrained MINLP problem can be solved efficiently by NSGA‐II to generate a set of feasible nondominated solutions in the range desired by the decision‐maker in a single run of the algorithm. The evolution of the entire set of nondominated solutions helps the decision‐maker to make a better choice of the appropriate design from among several alternatives. The large set of solutions also provides a rich source of excellent initial guesses for solution of the same problem by alternative approaches to achieve any specific target for the objective functions. Copyright © 2006 Curtin University of Technology and John Wiley & Sons, Ltd.

Petri‐net based integer programs for synthesizing optimal material‐transfer procedures in pipeline networks

Transferring materials through the pipeline network is a basic operation in almost every batch chemical plant. Traditionally, the tasks for conjecturing the needed operation steps are carried out manually on an ad hoc basis. This approach is often time‐consuming for industrial processes and, furthermore, the resulting recipe may be error‐prone. The aim of this paper is thus to develop a systematic strategy to generate the optimal operating procedures with Petri‐net based integer programs. Specifically, the shortest material‐transfer routes are selected on the basis of Petri‐net representation of the path structure in pipeline network. The equipment models are then incorporated into this path model to create a complete system model. An integer program can therefore be constructed accordingly to identify the detailed operation steps. Finally, a realistic example is presented at the end of this paper to demonstrate the effectiveness and correctness of the proposed approach.

Heat integration of multipurpose batch plants using a continuous-time framework

Presented in this paper is a continuous-time mathematical formulation for optimization of heat integrated batch chemical plants. This formulation is applicable to both multipurpose and multiproduct facilities in which opportunities for direct heat integration exist. It is assumed that thermal driving forces and heat duties between operations identified as potential heat integration candidates are sufficient. These operations can either belong to the same batch plant or distinct batch facilities within the same site. Two scenarios are explored in this paper. The first scenario entails a situation where energy requirement is dependent on batch size, which is allowed to vary with distinct task occurrences within the time horizon of interest. The second scenario is based on fixed batch sizes in which the duty requirement is specified as a parameter. In the first scenario, the resulting formulation is initially cast as a nonconvex mixed integer nonlinear program (MINLP), which is linearized exactly to yield a convex MILP problem. This linearization is not necessary in the second scenario, as the resulting model is readily an MILP problem. A literature example and a case study are used to demonstrate the effectiveness of the formulation.

A combined fuzzy set theory and MILP approach in integration of planning and scheduling of batch plants—Personnel evaluation and allocation

This paper presents the application of fuzzy set theory (FST) within the context of integrated planning and scheduling. Fuzzy set theory provides a framework for modelling uncertain or ambiguous information, which is commonly encountered in industry. A typical example of an industrial situation wherein uncertainty cannot be avoided due to the type of data required is operator evaluation (grading). This usually requires information on experience, expertise, responsibility, age, etc., which tend to be more qualitative than quantitative, and involve high levels of imprecision. Consequently, traditional mathematical procedures fail to address this issue effectively. Since human intervention is crucial in the operation and performance of batch chemical plants, the issue of operator evaluation demands attention. It is generally accepted that operators with different skills will have different process understanding and response times, which will eventually impact on plant performance. Moreover, different plants will have different requirements in terms of the aforementioned evaluation criteria. Therefore, efficiency of any operator evaluation procedure is entirely dependent on the quality of operator allocation to different plants. Another aspect that should not be overlooked when dealing with operators, is remuneration, which is determined by the grade. Operators with high levels of skill (experience) and expertise (education) tend to command high salaries. Therefore, they should only be hired on mandatory rather than discretionary grounds. The presentation made in this paper demonstrates the application of FST in dealing with qualitative features of plant personnel as well as plant requirements in order to maximise plant performance, whilst accounting for the financial aspects. The FST output can then be incorporated in an MILP formulation to determine the optimal allocation of operators to different plants. The second part of this series presents the impact of personnel allocation on the overall integrated planning and scheduling framework.

Design of Pipeless Chemical Batch Plants with Queueing Networks

The problem of designing pipeless batch plants is tackled using a multiclass re-entrant queueing network model. The overall design procedure is divided into three hierarchical phases: the preliminary phase, neighbor search algorithm (NSA), and greedy mean value analysis (GMVA). In the preliminary phase, the types of products and stations, as well as the capacities of equipment items, are determined. GMVA, which is a generalization of conventional MVA, finds the optimal job constitution for the system within a short computer processing unit (CPU) time, after the station configuration is fixed, whereas the station configuration is successively improved by NSA. The moderate time complexity of the proposed method extends the range of applications to practical-sized problems. An alternative design approach that is based on the decomposition approximation and formulated as a mixed integer nonlinear programming (MINLP) problem is also developed. The effectiveness of the method is illustrated through the examples of medium-sized pipeless plant design problems. Simulation studies using a commercial simulation package are described and observed to support the validity of the analytic method.

Automated solution for control recipe generation of chemical batch plants

Control recipe represents the detailed instructions to operate chemical batch plants. The design of complete and accurate control recipe is a complex process and requires continuous modifications to cope with the dynamic changes in market demands and product specifications. This paper presents automated solution called AOPS to synthesize master recipe and generate the corresponding control recipe. Master and control recipe are defined on the basis of recipe formal definition language (called RFDL), which enables the smooth and systematic mapping between operating procedures and domain knowledge. Topology editor and user interface are designed and used to capture plant design model and the associated domain knowledge, and to visualize and validate the generated control recipe. A case study of two-stage polymerization batch plant is used to illustrate the proposed solution.

Modeling the Energy Consumption of Chemical Batch Plants: Bottom-Up Approach

In a previous paper, the modeling of chemical batch plants with the help of a top-down model was presented (Bieler et al. Ind. Eng. Chem. Res. 2003, 42, 6135−6144). This approach was not applicable to multipurpose batch plants with highly varying production because of the high variability between products and the complexity of such plants. In the present paper, a bottom-up model is proposed, and its applicability to a multipurpose batch plant is investigated. Extensive measurements of single apparatus and unit operations were conducted, leading to simple, adaptable models for the consumption of steam, electricity, and brine. These single-apparatus and single-unit-operation models are summed according to the production schedule and the corresponding process step procedures or the production records. This leads to a bottom-up model of a complete multipurpose production plant. The energy consumption of the whole plant was modeled for 1 and 2 days, as well as for 1 week and 1 month. The comparison of the bott...

Recipe formal definition language for operating procedures synthesis

This paper presents a recipe formal definition language, called RFDL, which is used to synthesize operating procedures of chemical batch plants. RFDL statements are composed of sequence of keywords called tokens. These tokens are classified and linked to plant structure and domain knowledge so that it can be used to transform master recipe to the corresponding control recipe. RFDL editor and parser are proposed to synthesize master recipe in view of the predefined RFDL statements. The corresponding control recipe is automatically generated from master recipe on the basis of RFDL statements. A case study is used to show the utilization of the proposed recipe formal language to synthesize operating procedures of batch plants.