A Course on Process Design and Operation in an Engineering Technology Program (original) (raw)
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Process control: modeling, design, and simulation
2003
There are a variety of courses in a standard chemical engineering curriculum, ranging from the introductory material and energy balances course, and culminating with the capstone process design course. The focus of virtually all of these courses is on steady-state behavior; the rare exceptions include the analysis of batch reactors and batch distillation in the reaction engineering and equilibrium stage operations courses, respectively. A concern of a practicing process engineer, on the otherhand, is how to best operate a process plant where everything seems to be changing. The process dynamics and control course is where students must gain an appreciation for the dynamic nature of chemical processes, and develop strategies to operate these processes.
A step-by-step approach to advanced process control : Improving process control
Hydrocarbon Processing, 2003
Model-based advanced process control strategies are common applications in the hydrocarbon processing industry. A close look at the references available in literature reveals that the overwhelming majority of these projects are concentrated in refineries and in some specific petrochemical plants (e.g., ethylene). This is due to the excellent economic returns demonstrated in an abundance of experience dating back to the eighties. Nevertheless, APC and optimization practices may also greatly benefit relatively smaller plants. Although previous experience may be found on a few sites, process owners don’t have the same perception of a “must-do technology” as with other applications (FCC, crude units, hydrocrackers, etc.).
2006
The debutanizer column is an important unit operation in petroleum refining industries. The top product is liquefied petroleum gas and the bottom product is light naphtha. This system is difficult to handle. This is because due to its non-linear behavior, multivariable interaction and existence of numerous constraints on its manipulated variable. Neural network techniques have been increasingly used for a wide variety of applications. In this book, equation-based multi-input multi-output (MIMO) neural network has been proposed for multivariable control strategy to control the top and bottom temperatures of the column. The manipulated variables for column are reflux and reboiler flow rates, respectively. This neural network model are based on multivariable equation, instead of the normal black box structure. It has the advantage of being robust in nature while being easier to interpret in terms of its input-output variables. It has been employed for set point changes and disturbance changes. The results show that the neural network equation-based model for direct inverse and internal model approach performs better than the conventional proportional, integral and derivative (PID) controller.
Process Systems Analysis and Control - Donald R. Coughanowr - 3rd
Process Systems Analysis and Control, Third Edition retains the clarity of presentation for which this book is well known. It is an ideal teaching and learning tool for a semester-long undergraduate chemical engineering course in process dynamics and control. It avoids the encyclopedic approach of many other texts on this topic. Computer examples using MATLABänd Simulink¨have been introduced throughout the book to supplement and enhance standard hand-solved examples. These packages allow the easy construction of block diagrams and quick analysis of control concepts to enable the student to explore Òwhat-ifÓ type problems that would be much more difcult and time consuming by hand. New homework problems have been added to each chapter. The new problems are a mixture of hand-solutions and computational-exercises. One-page capsule summaries have been added to the end of each chapter to help students review and study the most important concepts in each chapter.
7 th European Symposium on Computer Aided Process Engineering -ESCAPE17
During the last decade, a major shift has begun in chemical industry, since there is an urgent need for new tools that are able to support the optimization of operating technologies. This trend is driven by the new tools of information technology. Approaches of this shift differ from company to company but one common feature is that communication between design, manufacturing, marketing and management is centered on modeling and simulation, which integrates not only the whole product and process development chains, but all the process units, plants, and subdivisions of the company. These approaches are under continuous development. Among the wide range of possible improvements, this paper focuses to two frequent imperfections: (i) developed and refined process models are used only in advanced process control system (APC) integrated into distributed control system (DCS) and operator training systems (OTS), and not for detailed analysis and optimization, and (ii) optimal process operating points of these chemical plants are adjusted only at the design and test phase of a new technology, but optima moves with time, new catalyst system, lower price of reactants, claim for new or higher purity products, etc. The aim of this paper is to review, how to manage process optimization, and to show our process simulator based on the chemical engineering model of the technology. This paper will present a case study to demonstrate the technological and ecological benefits of the analysis and optimization of an B. Balasko et al. operating multi-product polymerization plant. The models of advanced process control system (APC) and reactor cascade were implemented in MATLAB ® Simulink ® environment, as a powerful and popular dynamic simulator.
Advanced Control of a Complex Chemical Process
Brazilian Journal of Chemical Engineering, 2016
Three phase catalytic hydrogenation reactors are important reactors with complex behavior due to the interaction among gas, solid and liquid phases with the kinetic, mass and heat transfer mechanisms. A nonlinear distributed parameter model was developed based on mass and energy conservation principles. It consists of balance equations for the gas and liquid phases, so that a system of partial differential equations is generated. Because detailed nonlinear mathematical models are not suitable for use in controller design, a simple linear mathematical model of the process, which describes its most important properties, was determined. Both developed mathematical models were validated using plant data. The control strategies proposed in this paper are a multivariable Smith Predictor PID controller and multivariable Smith Predictor structure in which the primary controllers are derived based on Internal Model Control. Set-point tracking and disturbance rejection tests are presented for both methods based on scenarios implemented in Matlab/SIMULINK.
Chemical process control education and practice
IEEE Control Systems Magazine, 2001
C hemical process control textbooks and courses differ significantly from their electrical or mechanical-oriented brethren. It is our experience that colleagues in electrical engineering (EE) and mechanical engineering (ME) assume that we teach the same theory in our courses and merely have different application examples. The primary goals of this article are to i) emphasize the distinctly challenging characteristics of chemical processes, ii) present a typical process control curriculum, and iii) discuss how chemical process control courses can be revised to better meet the needs of a typical B.S.-level chemical engineer.