Optimal operation and control of intensified processes — challenges and opportunities
Introduction
In a highly competitive economic global environment, fomented by variability and extensive information exchange, companies must adapt and embrace changes in order to survive. Manufacturing facilities are required to be more flexible to accommodate the needs of dynamic markets. Frequent variations in raw material compositions, together with prices and demand fluctuations, give much more emphasis on process dynamics [1]. This raises the need for incorporating process dynamics in decision-making processes in order to guarantee optimal operations. A more sustainable industrial development should be pursued as environmental pressures increase, including carbon dioxide emission consideration and restrictive waste disposal regulations [2]. In this context, Process Intensification (PI) and Process Systems Engineering (PSE) emerge as tools to address current challenges in the operation and optimization of manufacturing facilities.
A general definition for process intensification has been given by AI Stankiewicz and JA Moulijn [3] as “any chemical engineering development that leads to substantially smaller, cleaner, and more energy efficient technology”. Several extensions to this definition have been proposed over the years in order to accommodate the progress and developments of the field [4,5,6]. Recently, Tian et al. [7••] summarized activities that results in intensified processes, including the combination of multiple process tasks or equipment into a single unit (e.g. membrane reactors, reactive distillations), the miniaturization of process equipment (e.g. microreactors), the operation of equipment in a periodic manner (e.g. simulated moving bed, pressure adsorption swing), and a tight process integration (e.g. dividing wall distillation). Judging from the growth of research interest in process intensification [1], it is clear that PI is a promising field that can enable a paradigm shift to the process industry, offering novel processing methods and equipment to achieve higher efficiency and safer operation. Nevertheless, challenges concerning the operability and controllability of intensified processes can prevent the successful implementation and optimal operation of such processes in the chemical industry.
Process Systems Engineering can contribute to this challenge by providing tools for a systematic approach for the design, optimization, control and operation of intensified processes. Over the years, the PSE community has developed novel representations and models that capture nontrivial features, enabling the simulation of complex processes, advances in process control, and improvements of decision-making processes for the operation of the chemical supply chain [2]. PSE has also contributed to the development of computationally efficient solution methods and software tools for complex optimization problems. Of particular interest to this article, PSE has proposed a representation of the hierarchy of decision-making process in the operations and control of a process industry, and has systematically addressed the challenges emerging in each level of the hierarchy [8,9]. Such representation is depicted in Figure 1, and encompasses the problems of planning, scheduling and control of manufacturing facilities.
In this article, an analysis of the tools and systematic approaches developed by the PSE community regarding the optimization of the decision-making processes in intensified processes is performed. In particular, an investigation of scheduling and control tools and its performance on intensified systems is conducted. First, on-going efforts on the operability and control of PI systems are briefly reviewed. Challenges in the control of intensified process are summarized, and opportunities for further enhancement of the control of PI systems are identified. Then, the integration among different decision-making stages (moving from control to scheduling decisions) are investigated. Interesting results from classical operations are presented, in the hopes that they will instigate efforts from the PI and PSE communities in exploring this direction. The integration of scheduling and control can be seen as the natural ‘next step’ on the optimization of intensified operations, and a new tool to guarantee efficient and clean manufacturing.
Section snippets
Control of intensified process
Process control can enable the safe, economical, and environmentally optimal performance of intensified processes. This capability has been demonstrated with a special focus on reactive distillation columns, which has received wide acceptance in the chemical industry [10]. An excellent overview of process control in process intensification has been presented in Nikačević et al. [1]. In this paper, the authors demonstrate how the majority of current research in control of intensified processes
Integration of scheduling and control
Scheduling of operations is a level above process control in the decision-making hierarchy. Scheduling can be defined as the problem of allocating resources, defining production sequences, equipment usage and assignment of tasks in an industrial plant, in order to achieve production targets while minimizing productions costs or production makespan. Such problems have been traditionally solved independently of the dynamic behavior of the system, although the solution of scheduling problems in
Conclusions
Process Intensification and Process Systems Engineering are moving together in the pursue of efficient manufacturing. Recent advances in PSE are naturally enabling advances in PI, and the modeling, simulation and control of intensified processes is becoming feasible. Nevertheless, significant challenges still need to be overcome, specially related to large-size and highly nonlinear dynamic behaviors. In this article, some promising areas that are providing tools and concepts to confront these
Conflict of interest statement
Nothing declared.
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgement
M.G.I. acknowledges financial support from the Food and Drug Administration under grand (DHHS - FDA - 1 U01 FD005295-01) and National Science Foundation under grant CBET 1159244, grant CBET 1839007 and grant CBET 1547171. L.S.D acknowledges financial support from CNPQ - Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brazil under grant 215670/2014-0.
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