Batch Process Plant Research Articles

A Scheduling Model Framework for Crane-Assisted Pipeless Process Plants

In this work, for the first time, we present a general framework for the scheduling of crane-assisted pipeless batch (and semibatch) process plant operations. Using the framework, we develop a prec...

Digitalization of an Automating Batch Production using SCADA in Paint Industry

Here Supervisory Control Acquisitions and Data is explained or discussed in this paper. In the paint industry – batch type production. It is to apply automation technique for the manufacturing of paints. In this paper, a computing design model for betterment of the functions of a batch processing plant which is capable of controlling all the analytical variables in the system and working durations of the previously created system operations which is applied to operations in a leading paint manufacturer plant. The focus of this work is a batch processing plant, multi-product, with wide list of projects, competing for many equipment stages at the manufacturing site. The production plant under discussion works in accordance with the principle of "order-based" production. Thus, this system can be segregated as a minimum term catalogue of a real case multi-product group processing plant. This paper also explains the digitalization of the process industries.

Automation of Batch Production in Paint Industry using PLC

This paper presents the development of automatic control over batch type production in the paint industry. The automation technique for the manufacturing of paint industry is applied wit0068 the help of programmable Logic Controller. In this task, the analytical compute model for optimizing the functions of the volume processing plant is capable of handling all possible decision variables in the setup. The synthesized function is used in a dominant paint construct plant. This workconcentrate plant is a multi-product, batch processing plant, with a ample variety of products, challenge for various process apparatus at the manufacturing site. The plant under consideration working in accordance with the principle of "order-based" production. Therefore, this application can be classified as a short-term organize of a real case multi-product module alter plant

Ontology-Assisted Engineering of Cyber–Physical Production Systems in the Field of Process Technology

Future cyber–physical production systems (CPPS) constitute a complex and dynamic network of services and plant components such as actuators and sensors. Consequently, the manual technical specification and design of these systems are a complex and time-consuming task involving extensive expert knowledge. In scope of CPPS, current approaches to reduce the engineering effort focus on manufacturing technology. There are initial approaches in the domain of process engineering available. However, these neither consider the knowledge-supported definition of recipe-based operations nor the assignment of dynamic service networks to process modules. The objective of this contribution is the design of a concept and a systematic approach to automate the engineering of batch process plants respecting dynamic service networks and process modules using a knowledge-based assistance system. For this purpose, a declarative recipe description is combined with an ontological model. This enables an automatic inference of technical requirements. Based on this information, a multistage orchestration algorithm selects and combines process modules and networked services to find appropriate engineering solutions. Finally, a comprehensive case-study demonstrates that the proposed approach is able to automate the target-oriented selection and combination of process modules and service networks.

Medium-term optimization-based approach for the integration of production planning, scheduling and maintenance

A medium-term optimization-based approach is proposed for the integration of production planning, scheduling and maintenance. The problem presented in this work considers a multiproduct single-stage batch process plant with parallel units and limited resources. An MILP continuous-time formulation is developed based on the main ideas of travelling salesman problem and precedence-based constraints to deal with, sequence-dependent unit performance decay, flexible recovery operations, resource availability and product lifetime. Small scheduling examples have been solved and compared with adapted formulations from the literature, based on discrete-time and global-time events, demonstrating the effectiveness of the proposed solution approach. Additional planning and scheduling problems have been proposed by considering several time periods. Multi-period examples have been efficiently solved by the model showing the applicability of the solution approach for medium-size problems.

Improving the software engineering of brew house plants by modularizing the control software

The development of control software for automation plants is a time-consuming task. With a modular structure of the control software, which is derived from a functional decomposition of plant and process, this paper presents an alternative approach to the widely used procedure “Copy, paste, modify” of the control software of a similar plant. The approach is applied on batch process plants, in particular 2-vessel brew house plants. Their plant topologies are modularized with regard to hardware, process and control. Based on the established modular structure of the plant control, software modules are developed. Generating the control software of new plants from an assembly of these software modules will reduce implementation effort and improve software reusability. As a proof of concept, a prototypical test plant is constructed and run with control software, which was automatically generated from a prototypical tool implementation.

Water-integrated scheduling of batch process plants: Modelling approach and application in technology selection

Efficient water management is becoming increasingly important in production systems, but companies often do not have any concrete strategies to implement. While there are numerous technological options for improving water efficiency in process plants, there is a lack of effective decision support to integrate water aspects in operational decision making. This paper is based on the premise that inclusion of water reuse and related technological decisions during scheduling of production operations is essential for reduction of industrial water consumption. We develop a water-integrated scheduling approach based on mathematical programming to capture the main characteristics of water flows in batch process plants. We model water quality with a practical and generic classification scheme to effectively include water reuse and treatment possibilities. The approach is able to quantify tradeoffs between production efficiency and water efficiency. We also illustrate the use of our approach in the evaluation of water reuse and regeneration technologies for typical process industry settings. Overall, our approach is able to integrate water reuse and regeneration in a relatively efficient manner, and can help reduce industrial water consumption in process industries.

Simultaneous Targeting and Scheduling for Batch Water Networks

Batch processes have received considerable attention from both industry and academia because of their flexibility and suitability to produce low volume, high value-added products (e.g., fine/specialty chemicals) and also as a result of the recent trend toward product-centered manufacturing globally. This paper presents an optimization technique for water network synthesis in batch processing plants. The mathematical formulation is a mixed integer linear program (MILP) based on the pinch-based automated targeting model (ATM) and a state-task network (STN)-based discrete-time scheduling model. Embedding the ATM in the scheduling framework enables simultaneous process scheduling and water minimization and the true minimum flow/cost targeting prior to detailed water network design. Two modified literature examples and an industrial case study on polyvinyl chloride (PVC) manufacturing are solved to demonstrate the application of the proposed MILP model. Results for the PVC case study show that significant wate...

Real‐time resource efficiency indicators for monitoring and optimization of batch‐processing plants

AbstractThis paper presents a framework for the definition and calculation of Real‐Time Resource Efficiency Indicators (REIs) for batch processes. The indicators are based on the allocation of resource and energy inputs to the individual batches and recipe operations as the basis for a comprehensive analysis and representation of the overall resource efficiency.The framework structures REIs into three categories describing (1) the efficiency of key unit operations, (2) the efficiency of the production of an individual batch, and (3) the overall system performance. Indicators from the last two categories can be propagated along the production process and can be aggregated vertically from units to sections and complete production sites. Chemical production processes often involve batch blending and splitting, separation processes, and continuous production steps. The framework allocates the contributions of the continuous steps to the batches and considers merging and splitting of batches. Thus, it yields a reliable and commensurate set of indicators that can be used to monitor and to optimize the resource efficiency of batch processing plants in real‐time in order to support the decision making process in daily operations. To demonstrate the approach, it is applied to a sugar production process that integrates batch and continuously operated unit operations.

A modeling strategy for integrated batch process development based on mixed-logic dynamic optimization

This paper introduces an optimization-based approach for the simultaneous solution of batch process synthesis and plant allocation, with decisions like the selection of chemicals, process stages, task-unit assignments, operating modes, and optimal control profiles, among others. The modeling strategy is based on the representation of structural alternatives in a state-equipment network (SEN) and its formulation as a mixed-logic dynamic optimization (MLDO) problem. Particularly, the disjunctive multistage modeling strategy by Oldenburg and Marquardt (2008) is extended to combine and organize single-stage and multistage models for representing the sequence of continuous and batch units in each structural alternative and for synchronizing dynamic profiles in input and output operations with material transference. Two numerical examples illustrate the application of the proposed methodology, showing the enhancement of the adaptability potential of batch plants and the improvement of global process performance thanks to the quantification of interactions between process synthesis and plant allocation decisions.

Integrated Process and Plant Design Optimisation of Industrial Scale Batch Systems: Addressing the Inherent Dynamics through Stochastic and Hybrid Approaches

This work explores stochastic and hybrid solution approaches for dealing with the problem of integrated batch process development and plant design. The simultaneous optimization of batch process synthesis, task allocation and plant design has been formulated in the literature as a mixed-logic dynamic optimization (MLDO) problem, including dynamic control profiles, continuous variables, integers and Booleans as degrees of freedom. In industrial scale situations, this formulation leads to numerically intractable problems when mathematical programming solution strategies are used. So, this work presents a 2-step approach that combines a differential genetic algorithm (DGA) with a deterministic directsimultaneous solution that transforms the problem into a non-linear programming (NLP) problem. The core idea is to combine in the DGA chromosomes the multiple decisions that characterize the problem, and then to use the solution obtained for reducing the complexity of this highly non-linear problem, so it can be managed by standard deterministic solvers. A comparative study of the stochastic and hybrid strategies with the purely deterministic solution is made for the specific case of primary copolymerization for acrylic fibre production. The results show that local optimal solutions of the deterministic method can be beaten by the proposed optimization strategy, becoming a suitable option for solving cases of industrial size.

Total Site Heat Integration with Seasonal Energy Availability

Total Site Heat Integration (TSHI) approach has been extended to the Locally Integrated Energy Sectors (LIES) that include small scale industrial plants, renewable energy sources with variable supplies, and residential as well as commercial buildings as energy consumers with fluctuating demands. TS targeting methodology with Time Slices, which is comparable to Heat Integration for batch processes, have been introduced for a short-term, day-to-day analysis. Energy storage facility is integrated in the TS to address the problem of energy supply variation. However, previous studies have mostly dealt with the daily or short term variations. This work introduces an extended methodology for TS integration for the long-term energy supply and demand planning. The work is important in solving cases with a considerable temporal fluctuation of heat sources/sinks, in order to obtain more energy saving opportunities within a longer period of time (i.e., in the scale of weeks or even months). The energy fluctuation in processing plants may be caused by seasonal climate variations, long-term customer demands or even economic down-turn. Some other possibilities include operability issues and raw material availability. The methodology can also be implemented for district heating and cooling systems in countries with four seasons, assuming energy excess during the summer can be stored for use in other seasons e.g. during the winter. This methodology is demonstrated by a case study which involves integration of batch processing plants and space heating system together with cooling system which only operates during certain periods of time. Several scenarios which affect the long-term energy supply and demand have been assumed.

Particle Swarm Optimization Algorithm for a Batching Problem in the Process Industry

An improved particle swarm optimization (PSO) algorithm is proposed to solve a typical batching problem in a batch processing plant of the process industry. The batching problem (BP) is to transform the primary requirements for products into sets of batches for each task with the objective of minimizing the total workload. On the basis of some preliminary properties, a novel particle solution representation is designed for the BP. Unlike the ordinary idea of taking an objective function as the fitness function for PSO, the original objective function incorporated with a constraint function is to act as the fitness function of the PSO where the constraint and the objective functions are evaluated successively. Such a fitness function, together with a forward repair mechanism, makes it possible for a faster convergence. Further, for each iterative generation, a local search heuristic is used to improve the global best particle found so far. To verify the performance of the proposed PSO algorithm, the well-k...

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.

Design and Optimization of Indirect Energy Storage Systems for Batch Process Plants

One of the main difficulties for handling heat integration problems in batch plants is the time-dependent existence of hot/cold process streams during time period p. This article aims at proposing a generic method for synthesizing an indirect heat exchange network and its associated thermal storage policy for targeting the external utility in a batch plant. Therein, a heat transfer medium (HTM), originally staying in a cold tank, is used to absorb surplus heat from hot process streams, and the HTM with elevated temperature is temporarily stored in a hot tank. The accumulated hot HTM is then utilized to heat subsequent cold process streams, and the cooled HTM recirculates back into its source cold tank. The limitation of heat interchange between time-dependent hot/cold process streams can be relaxed by applying the recirculated heat transfer medium to carry out indirect thermal storage. A superstructure is proposed and the mathematical programming approach is applied for investigating the indirect heat exc...

Systematic Waste Minimization in Chemical Processes. 3. Batch Operations

The current drive toward ecosustainability has provided a strong impetus to implement waste minimization within the chemical industries. However, conducting a waste minimization analysis is expensive, time-consuming, laborious, and knowledge-intensive. In part 1 of this series [Halim, I.; Srinivasan, R. Systematic Waste Minimization in Chemical Processes: Part I. Methodology. Ind. Eng. Chem. Res. 2002, 41 (2), 196], we described a systematic methodology based on the Environmental Optimization (ENVOP) technique for waste minimization analysis of chemical plants. In part 2 [Halim, I.; Srinivasan, R. Systematic Waste Minimization in Chemical Processes: Part II. Intelligent Decision Support System. Ind. Eng. Chem. Res. 2002, 41 (2), 208], we reported on ENVOPExpert, an expert system that implements the methodology. While the focus in parts 1 and 2 were on continuous processes, in part 3 we extend the waste minimization methodology to batch process plants and describe its implementation as an expert system c...

Scheduling of batch plants: Constraint-based approach and performance investigation

Batch processing plants are attractive due to their suitability for the manufacturing of small-volume, high-value added products. Scheduling batch plants by using computer-aided systems is important for improving the plant productivity, since it harmonizes the entire plant operation efficiently to achieve production goals. However, the current scheduling approaches for batch plants are inadequate. This research develops a constraint-based model and system for batch-process scheduling and investigates their performance. The proposed constraint model analyses and brings together many scheduling constraints, adds new constraints and categorizes them according to their functionality. A computer scheduling system, Batch Processing Scheduler, is developed in C++ to apply the model. A number of examples have been devised to study the performance of our constraint-based approach. It is found that the approach can schedule complex plants and solve large-size problems by finding feasible solutions satisfying all imposed constraints, which include some hard ones such as those of finite wait time. It is also identified that the first feasible solution can be found very quickly, but much more time, even exponentially more, is required to find the optimal solution particularly for complex and large-size problems. Feasibility and limitations of the proposed methodology are demonstrated by the results.

Hybrid modeling and dynamic simulation of automated batch plants

Abstract A proposal is presented for the hybrid modeling and dynamic simulation of automated batch processing plants with dominant discrete-event behavior. The proposal encompasses current techniques for modeling continuous-time/discrete-event processes, synthesizing discrete-event controllers as well as the use of industrial standards for batch control. The result is a hierarchical-modular model of a plant in which process and control tasks are clearly differentiated. Implementation rules are established for a specific dynamic simulator capable of handling hybrid systems. The approach is demonstrated by building and verifying a complete model for an automated milk pasteurization plant. Results show that handling problems of realistic complexity is feasible using state-of-the-art technology.

Supervisory target control for hybrid systems

The problem of systematically synthesizing supervisory control laws that satisfy eventuality and efficiency requirements for hybrid systems modelled by hybrid automata is considered. Here, the efficiency requirement is specified by weighting the discrete transitions of the system. The optimization of the efficiency requirement is considered in the min-max sense due to the existence of disturbance inputs. Adopting a game theoretic approach, the high priority eventuality requirement is considered first and the class of controls, in which the low priority efficiency requirement should be optimized, is obtained. Then, a dynamic programming algorithm, which determines the value function of the optimization problem, is derived. A synthesis problem on a simplified batch process plant is considered to illustrate a potential application of the approach.

Improving the development of event-driven control systems in the batch processing industry. A case study

Abstract This paper presents the results of an academia-industry collaborative project whose main objective was to test novel techniques for the development of event-driven control systems in the batch processing (e.g., pharmaceutical, fine chemicals, food) industries. Proposed techniques build upon industrial standards and focus on (i) formal synthesis of phase control logic and its automatic translation into procedural code, and (ii) verification of the complete discrete-event control system via dynamic simulation. In order to test the techniques in an engineering environment, a complete discrete-event control system was produced for a benchmark batch process plant based on a standard development method employed by one of the industrial partners. The control system includes functional process specification, control architecture, distributed control system (DCS) proprietary programming code for procedural control at equipment, unit, and process cell levels, and human-machine interfaces. A technical assessment of the development method and the obtained control system was then carried out. Improvements were suggested using the proposed techniques in the specification, code generation and, verification steps. The project assessed the impact of these techniques from both an engineering and economic point of view. Results suggest that the introduction of computer aided engineering (CAE) practices based on the benchmarked techniques and a structured approach could effect a 75% reduction of errors produced in the development process. This translates into estimated overall savings of 7% for green-field projects. Figures were compared with other partners' experience. It is expected that the work load on a given project will shift, increasing the load on process engineers during the specification stage and decreasing the load on the software engineers during the code writing.