Multiproduct Batch Processes Research Articles

A functional data-driven approach to monitor and analyze equipment degradation in multiproduct batch processes

Equipment degradation is ubiquitous in the Chemical Process Industry (CPI), causing significant losses in efficiency, controllability, and plant economy, as well as an increased environmental fingerprint and additional operational safety risks. The case of fouling in heat exchangers, in particular, is well-known and pervasive but still hard to cope with, given the complexity of the underlying mechanisms and the difficulty of assessing its extension in real-time. This problem becomes even more complex in batch processes producing different products, where multiple recipes are used, bringing additional variability and new challenges to the analysis. In this work, we propose a functional data-driven approach for streamlining the analysis and monitoring of the progression of fouling taking place in heat exchangers in multiproduct batch processes. With the approach developed and presented in this paper, process analysis can be efficiently conducted by integrating historical data with engineering knowledge. Furthermore, a surrogate measure of fouling extension in heat exchangers is proposed, that can be readily implemented as an equipment health indicator (EHI) leading to a safer operation of the heat exchanger.

Freshwater minimization in multistage multiproduct batch processes through intentional waiting

The growing demand for industrial water has motivated techniques for water reuse within the ambit of production scheduling. In batch processes, these seek to minimize freshwater usage together with an economic objective, such as makespan. It may, however, be possible to increase reuse potential by compromising on the makespan objective and intentionally delaying the start of stages so as to enable better time synchronization. This work seeks to develop a scheduling methodology for batch processes that reduce freshwater requirements by intentionally waiting between stages. A genetic algorithm-based formulation is proposed to develop such a production schedule where delays up to a maximum allowed waiting duration are introduced if it enables water reuse. The proposed scheduling strategy is demonstrated using a textile industry case study. Our results show that, on average, a 20% decrease in freshwater usage can be obtained for a single-batch campaign while for a multi-batch campaign, the savings in freshwater is nearly 59% for a 19% increase in makespan.

A simple strategy to maximize water-reuse in multistage, multiproduct batch processes

Many countries are currently facing a severe water crisis. Further, in various industrial sectors, small and medium enterprises operate multistage, multiproduct batch processes that require large quantities of water. This work is motivated by the inability of such enterprises to deploy complex water-reuse strategies since their plants are neither well instrumented nor automated. The design and operational strategies of water reuse networks for multistage, multiproduct batch processes is inherently challenging due to the underlying discontinuities and other nonlinearities. In this paper, we propose an easy-to-implement water-reuse strategy that utilizes intermediate storage tanks with finite capacities. The strategy relies on a simple heuristic that maps discharge from specific stages to be stored in corresponding tanks. A fallback sequence is also provided in case the tank cannot accommodate the entire discharge volume. An analogous scheme for reusing water from the tanks in appropriate process stages is also proposed. In this paper, we demonstrate using a textile industry case study that this simple reuse strategy can result in significant water savings. For this, we develop an optimal production schedule using a genetic algorithm that simultaneously minimizes production makespan and the freshwater consumption. Our results show that for a set of 4 products with up to 14 production stages, 76% of the freshwater could be reduced if 13 tanks can be deployed. Even with 2 tanks, the proposed strategy results a 42% reduction of the freshwater requirements.

Optimal production and maintenance scheduling for a multiproduct batch plant considering degradation

Performance decay due to asset degradation is an important constraint in industrial production and therefore needs to be actively considered. This paper focuses on short-term scheduling for multiproduct batch processes with sequence-dependent degradation and is motivated by a case study in which the sequence of multiple-grade batch runs impacts evolution of fouling. A continuous-time scheduling formulation is proposed to incorporate realistic features of the case study processes. The precedence scheduling concept for the sequential process is employed to model sequences of multiproduct orders and maintenance and is implemented using the general disjunctive programming method. The scheduling formulation is applied to the case study and further analyzed through comprehensive computational tests, which illustrates the efficacy of the proposed formulation.

Heat integration of a multi-product batch process by means of direct and indirect heat recovery using thermal energy storage

Process heat integration in batch processes is a complex problem given their time dependent behavior and need for thermal energy storage. In case of multi-product batch processes the complexity increases further. Due to its intricacy, the topic has so far been hardly addressed in literature. This article presents a practical case study on heat integration of such complex batch processes in a medium-sized European textile plant using pinch analysis. The maximum heat recovery potential during different time slices of a day is estimated at 21–43% of the heat demand of the company. The processes are first optimized for direct heat recovery and a heat exchanger network is recommended with a maximum recovery potential of 85 GJ per day. The residual process requirements are then analyzed for indirect heat recovery by means of thermal energy storage. A closed intermediate loop and heat storage system with four temperature levels is recommended with an additional recovery potential of 17 GJ per day. The proposed heat exchanger and storage designs are both found to be cost‐effective with payback times ranging from 1 to 5 years. In spite of being technically rather demanding, more attention should be paid to thermal energy storage in batch processes since they are likely to offer more attractive additional energy saving potentials in industry. The approach provides an effective way of identifying practical solutions for heat integration in complex multi-product batch processes.

Addressing the challenges of implementing safety instrumented systems in multi-product batch processes

Adapting the requirements of IEC 61511 to a batch system can be frustrating, particularly for multi-product units. While a Safety Instrumented System (SIS) for continuous operation is often a straightforward detect-decide-act loop, implementing a SIS for a batch system may involve multiple safety functions, time- or state-dependence, intricate calculations, or complex installations. Relationships between the SIS elements and the basic process control system (BPCS) must be tightly managed, providing both for the safety of the unit and its ability to operate without spurious trips or other hindrances. These issues are further complicated when multiple products requiring different functions or setpoints are produced in the same SIS-protected batch unit.This paper will discuss the challenges particular to the design, operation, and maintenance of a SIS in multi-product batch operations and present practical options for successfully resolving the concerns. A key insight into successful adaptation is treating the batch SIS as a “permission” system for the BPCS to operate. Although many items can be addressed through clever engineering practices, sustainable success relies on proactive, robust management of the safety lifecycle.

A continuous time model for a short-term multiproduct batch process scheduling

In the chemical industry it is common to find production systems characterized by having a single stage or a previously identified bottleneck stage, with non-identical parallel stations and with configuration or preparation costs depending on the production sequence. This paper proposes a mixed integer production scheduling model that seeks to identify lot size and product sequencing with the objective of maximizing profit. In addition, it considers multiple typical industry conditions, such as penalties for noncompliance and out of service periods of units (or stations) for preventive maintenance activities.The model was validated with real data from a chemical company and a 12% reduction in the total cost of production and a 19% increase in the estimated profit. In addition, we applied the model to 155 instances generated using Monte Carlo simulation, based on historical data from the same company, to analyze its performance.

Rule-based scheduling of multi-stage multi-product batch plants with parallel units

Abstract A novel rule-based model for multi-stage multi-product scheduling problem (MMSP) in batch plants with parallel units is proposed. The scheduling problem is decomposed into two sub-problems of order assignment and order sequencing. Firstly, hierarchical scheduling strategy is presented for solving the former sub-problem, where the multi-stage multi-product batch process is divided into multiple sequentially connected single process stages, and then the production of orders are arranged in each single stage by using forward order assignment strategy and backward order assignment strategy respectively according to the feature of scheduling objective. Line-up competition algorithm (LCA) is presented to find out optimal order sequence and order assignment rule, which can minimize total flow time or maximize total weighted process time. Computational results show that the proposed approach can obtain better solutions than those of the literature for all scheduling problems with more than 10 orders. Moreover, with the problem size increasing, the solutions obtained by the proposed approach are improved remarkably. The proposed approach has the potential to solve large size MMSP.

Production Decision Support System: Real-life Emulsion Plant Case Study

As the world becomes increasingly globalised, chemical manufacturers have to make timely decisions and right strategies in order to stay competitive in marketplace. Numerous formal techniques have been suggested for aggregate planning but industry finds these models complicated and lack practicality. The objective of this paper is to develop an industry adoptable Production Decision Support System (PDSS) for multi-product and multiprocessor batch industries. The construction of this system is initiated with the development of a general batch production planning framework which will be eventually translated into an algorithm tool. This tool will be able to support the investigation of required sales forecasts against plant capacity by providing a complete overview with different scenarios on multi-product batch processes, thus improving effectiveness and efficiency of operational decision-making, as well as assisting in strategy establishment. The production contributory factors that are being considered for plant capacity include cycle time, batch size, plant availability, number of production lines and product mixes. The practicality and beneficial use of this PDSS system is then demonstrated through a real-life emulsion plant where its production capacity has been successfully stretched giving additional volume which has translated into additional revenue of RM 10 M to the company.

Optimal Scheduling of Flowshop Batch Process for Zero-Wait and No Intermediate Storage Transfer Policy

The scheduling zero-wait (ZW) and no intermediate storage transfer (NIS) policy of multi-product batch processes in order to produce a number of low volume high value-added chemical products because of its economic impact. It involves various parameters such as makespan (completion time) which is recognized as one of the important design parameter as it helps to decide for the best scheduling design and normally used as the main parameter for selecting the optimal production sequence which involves various parameters such as batch process recipes, sequence of production and transfer policy for product intermediates. In this paper, we present a development, solution and computational performance evaluation of optimal scheduling for multiproduct batch process with two commonly used transfer policies namely zero wait (ZW) and no intermediate storage (NIS) by using computer program language software (Java) which simplify and improve the determination of Makespan and select the optimum sequence due to the minimum Makespan.

An evolutionary Lagrange method for batch process optimal design

The optimal design of the multiproduct batch processes is one of the most important decision-making problems in the manufacturing industry. This decision-making problem can be formulated as a mixed-integer non-linear programming (MINLP) problem. In this paper, we propose an evolutionary Lagrange method to deal with such an MINLP problem. The computational results demonstrate the effectiveness of the algorithm in solving the optimal design problem of the multiproduct batch processes.

Optimization of multi-product batch plant design under uncertainty with environmental considerations

This paper presents a framework for the design and optimization of multi-product batch processes under uncertainty with environmental considerations. The uncertainties and environmental impacts are discussed. The profit and environmental impacts are considered as bi-objectives for batch plant design. The problem, thus, is formulated as a multi-objective stochastic programming problem. It can be converted into a single-objective two-stage stochastic linear programming problem using the weighted aggregation method. To solve the two-stage stochastic programming, we introduce both Monte Carlo sampling for the entire domain of the distribution function and the feasibility cut method based on dual theory in Benders’ decomposition. The detailed algorithm for problem-solving is presented. A numerical example is presented to illustrate the proposed framework.

More on Completion Time Algorithms for Intermediate Storage Tanks in Multiproduct Batch Process Scheduling Using Matrix Representation

In batch process scheduling, the introduction of intermediate storage tanks between process stages result in increasing the efficiency and productivity of the process. The main purpose of this is to minimize the process completion time, i.e. makespan. However, it is important to determine the right number and respective locations of the intermediate storage to ensure optimal investment and operational cost. The generally adopted transfer policies for handling intermediate storage tanks in batch processes are unlimited intermediate storage (UIS) and finite intermediate storage (FIS). In this work, new completion time algorithms for UIS and FIS transfer policies are proposed using matrix representation. The objective of the proposed approach is to calculate the makespan for all possible production sequences for given batch product recipes while at the same time determining the number and location of intermediate storage tanks required. With the assumptions that the intermediate storage could be used to store any intermediate product, the method is verified against the Gantt chart method and the results agreed well. A number of different product recipes are analyzed using a developed computer code. This enables the proposed algorithms to produce solution options for large size batch scheduling problems.

Particle Swarm Optimization Algorithm for a Campaign Planning Problem in Process Industries

Campaign planning problem (CPP) is to determine the number and length of campaigns for different products over a planning horizon such that the setup and inventory holding costs are minimized. This problem can be found frequently in a multiproduct batch processing plant in the processing industry, such as chemical or pharmaceutical industries. This paper investigates a typical CPP and proposes a hybrid approach of heuristic and particle swarm optimization (PSO) algorithms where the PSO is applied to solve one subproblem with binary variables while the heuristic is applied to the other subproblem with remaining variables by fixing binary variables. As for the evaluation of particles, we take the whole objective function of the primal problem as a fitness function which can be calculated by solving the two subproblems. In implementing the PSO, by designing a “product-to-period” representation for a discrete particle, we redefine the particle position and velocity which are different from the standard PSO. Furthermore, a new strategy is developed to move a particle to the new position. To escape from local minima, a disturbance strategy is also introduced during the iteration process of the PSO. Computational results show that the proposed PSO may find optimal or near optimal solutions for the 180 instances generated randomly within a reasonable computational time.

New completion time algorithms for sequence based scheduling in multiproduct batch processes using matrix

Scheduling of batch processes involves various parameters such as batch process recipes, sequence of production and transfer policy for product intermediates. All these parameters directly or indirectly affect the makespan, i.e. completion time of a batch process. The scheduling approaches reported in past literatures mostly refer to the use of complex mathematical models for determining makespan. These models require good understanding in formulating the problem using programming techniques to find the optimal solution. The current work proposes new completion time algorithms for multiproduct batch process with two commonly used transfer policies namely zero wait (ZW) and no intermediate storage (NIS). The batch process recipes are presented in the form of a matrix which is then used to solve for the completion time using developed algorithms. Rearrangement of the matrix rows reflects the changes in production sequences possible for the specified batch process recipes which then enable the makespan to be determined accordingly for the different sequences. Designer is then provided with the production sequence options with their corresponding makespan from which a selection could be made.

Fuzzy-based methodology for multi-objective scheduling in a robot-centered flexible manufacturing cell

A fuzzy logic based methodology for generating the sequence of part movements in a multi-product batch processing through a computerized machine cell is presented in this paper. A number of production objectives are taken into account. Two fuzzy based strategies: fuzzy-job and fuzzy-machine are proposed and their performance is compared to two well known dispatching rules such as SPT (Shortest Processing Time) and WEED (Weighted Earliest Due Date). The sequencing algorithm was implemented on a standard personnel computer and the scheduler was interfaced to a robot controller for implementing loading and unloading strategy within the cell. The proposed fuzzy-based methodologies especially fuzzy-job shows a superior performance compared to the traditional dispatching rules considered.

Scheduling of Uncertain Multi-product Batch Processes Under Finite Intermediate Storage Policy

There are various uncertainties in the production scheduling of the real-world applications. And in multi-product batch processes, the intermediate storage often can be used to increase plant productivity and operational efficiency. In this paper, the scheduling mathematical model for multi-product batch processes under Finite Intermediate Storage (FIS) policy with uncertain processing time has been established based on fuzzy programming theory. And the Maximum Membership Functions of Mean Value method is applied to convert the fuzzy scheduling model to the general optimization model. Furthermore, a fuzzy immune scheduling algorithm combined with the feature of the Immune Algorithm is proposed, which can prevents the possibility of stagnation in the iteration process and achieves fast convergence for global optimization. The effectiveness and efficiency of the fuzzy scheduling model and the proposed algorithm are demonstrated by simulation results.

Response surface analysis of a multi-product batch processing facility using a simulation metamodel

In a multi-product batch production environment, managers and engineers must have a robust understanding of the impact of both operational and environmental variables and their interactions on process throughput. This insight is never more important than when a new facility is designed and built. In this study of a particular company in the specialty chemicals industry, a general simulation metamodel was developed in order to understand better how plant design parameters could be optimized in order to maximize plant throughput under certain environmental conditions and with certain asset investment constraints. The simulation metamodel developed is essentially a deterministic, analytical representation of a detailed, discrete-event, stochastic simulation model that was previously developed and validated to study operational and scheduling issues. The development of the simulation metamodel for plant design, as a function of capacity and throughput, is described in this paper. This study exemplifies the application of detailed, discrete-event, stochastic simulation models to derive deterministic, analytical response surface representations for purposes of plant design and throughput analysis in the process industries.

Analyzing throughput and capacity of multiproduct batch processes

Analyzing throughput and capacity of multiproduct batch processes

Analyzing throughput and capacity of multiproduct batch processes

A detailed discrete-event simulation model is extensively utilized by a major chemical process facility to evaluate new plant designs. This model is further exploited using known statistical techniques to provide valuable sensitivity analysis feedback to engineers for process analysis and improvement. A framework is proposed to guide the implementation and application of multivariate statistics and simulation metamodels to analyze simulation output of multiproduct batch processes.