Process Systems
Interfacial Phenomena Thermodynamics and Separation
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Process Design And Synthesis
Process Operations
Process Control
Integration Of Design, Control And Operations
Supporting Tools And Methods For Process Systems Engineering

Process Design And Synthesis
The creation, evaluation and optimization of a chemical process is the central task in process systems engineering. In industry, the goal has been reduction of the design time as well as the development of better designs that incorporate life cycle features of the process. These features include, of course, a highly profitable and competitive process, as well as a process that is easy to control and operate. Moreover, it must be environmentally benign and safe. The research tasks involved in this task take the following categories:

  • Process Simulation
    Typically the bread and butter of industrial designs, this task deals with the analysis of a given process. Challenges in process simulation include the incorporation of more difficult and detailed process models. These include modeling of process dynamics as well as steady state, incorporation of transport models for separation, the simulation of highly nonideal systems with multiple phases, and the development of rigorous, first principle reactor models. These models require simulators to evolve from modeling and solving algebraic systems of equations to differential algebraic models and also to consider PDEs. In addition, the availability and application of optimization methods has led to a powerful extension of simulation tools.
  • Energy systems
    This task includes the synthesis of network structures for heat exchanger networks, heat pumps and the integration with utility systems. The impact on the process is a direct reduction in energy consumption and more efficient energy utilization.
  • Separation
    Research in this area deals with the synthesis of sequences for the separation of nonideal mixtures, possibly with multiple phases and highly nonlinear process behavior. Process impacts include novel approaches and simpler processes for separation of solvents, byproducts and wastes, with a direct result on process efficiency and environmental impact. Another important trend is the design of reactive distillation systems that can sometimes lead to very significant cost savings.
  • Reactors
    Reactors are inherently characterized by complex nonlinear behavior. Moreover, the process chemistry has a leading influence on raw material conversion and on the overall process design. The synthesis of novel reactor network structures leads to waste minimizing processes and greater efficiency in the conversion of raw materials to desired product.
  • Design under uncertainty
    Over the life cycle of the process, input conditions and product demands change, feedstock and product specifications may vary and the process will be subject to short and long term uncertainties. Moreover, process models are also subject to uncertainty.

The challenge therefore is to develop a design that is tolerant to levels of uncertainty and exhibits a profitable expected performance. An important case is also the one of processes under multiperiod operation that are subjected to a finite number of process variations.

Process Operations
To complement the role of process design and synthesis, research in process operations seeks to improve existing operating processes. Through the development of strategies and analysis tools, improvements can be found through on-line optimization of a process, scheduling of operating strategies, changeovers and interactions between different processes, and overall planning of product productions to meet market demands. The research tasks involved in this task take the following categories.

  • Flexibility and Operability
    This task is devoted to the development quantitative measures of process flexibility as well as strategies that improve these measures for chemical processes. Here improvements in both design and operation can be considered to increase the process' tolerance to uncertainty. This also allows the process to deal with a wider range of operations and production scenarios. The formulation of flexibility problems, either in terms of deterministic or stochastic measures, leads to large, complex optimization problems and with challenges for the application on realistic processes.
  • On-line Optimization
    With short term (e.g., hourly) changes in feedstock and product demands, the availability of detailed process models and powerful optimization tools, it is now possible to optimize steady state models on-line and to readjust the setpoints of the control system. This leads to processes that can adapt to daily fluctuations in inputs and uncertainties. These can therefore lead too much higher profits. The current challenge is to deal with dynamic models in addition to steady state cases and also to provide a tighter coupling to the process control system.
  • Process Scheduling
    Scheduling of batch and continuous processes can have a major impact on the overall profitability of a process, as well as on the timely delivery of products. Major problems include sequencing, scheduling of equipment utilization and maintenance over a planning horizon, and inventory considerations of a process. Such problems form perhaps difficult combinatorial optimization problems but also contribute to high payoffs. Moreover, the results of this task have a major impact on the local operation of the process, and strong interactions exist between the scheduling, design and operation of the process.
  • Planning and Supply Chain Management
    Production planning and supply chain management provide the decision support systems for the logistics in the long range operation of networks of plants, and their coordination with marketing and business considerations. These problems give rise to very large multiperiod optimization problems where a major challenge lies in the effective aggregation of more detailed scheduling and operational models.

Process Control
Process control has evolved into a strong discipline in process systems engineering. Traditionally this has been characterized by single loop PID controllers with incremental advances that lead to advanced elements in the control system. More recently, concepts from optimization, mathematical analysis and nonlinear dynamics have played important roles in developing more efficient and superior control strategies. Areas of research can be classified as follows:

  • Model Predictive Control (MPC)
    Developed in the late 70s, MPC has shown significant advantages over structured PID control loops and has become the most widely used multivariable control strategy in industry. This approach is a generic strategy applied to large classes of unit operations, but was developed only with linear process models (usually derived empirically). Only recently have theoretical properties of these controllers been developed. Moreover, the discovery of many interesting properties for control and identification has led to direct results in tuning and design of these control systems in industry.
  • Nonlinear Control
    All processes are nonlinear and in many cases, linear model-based controllers are no longer satisfactory. To deal with this, geometric linearization strategies have been developed and lead to powerful insights in the design of control structures. Moreover, model predictive control can also be extended directly to deal with nonlinear dynamic models. Again, properties relating to the stability, robustness and performance of these controllers still need to be explored. Also, industry has had significant successes with these controllers on batch and semi-continuous processes.

Integration Of Design, Control And Operations
Finally, the ability to develop large steady state and dynamic process models and to solve large and complex optimization problems naturally leads to problem formulations that directly consider the interactions of process design, control and operations. The results of this approach lead to powerful synergies among these tasks, better performance of the process and improvements in profitability, efficiency and environmental impact. Challenges related to this approach include the modeling of quantitative metrics for control, flexibility and operability and the solution of large optimization problems with both continuous and discrete decision variables.

Supporting Tools And Methods For Process Systems Engineering
All the above areas in process systems engineering are supported by a large number of tools and algorithms which are the subject of active research efforts. These include modeling systems for simulation, optimization and control; algorithms for algebraic/differential equations and integral/PDE systems; algorithms for linear and nonlinear discrete and continuous optimization; algorithms for global and stochastic optimization; information management systems and data bases; advanced computer architectures for parallel computation.




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