Search

The work proposes two different approaches where the first one is based on the tools of the system theory and the other is strongly related to the principle of heat balance, in order to analyze the abnormal phenomena of the continuous styrene polymerization reactors, i. e. the multiplicity behavior in the wide range of operating conditions. More precisely, the multiplicity behavior of polystyrene production in a continuous stirred tank reactor (CSTR) is carried out by the numerical simulations through the Van Heerden diagram and the phase plane. Furthermore, the bifurcation diagrams in terms of two different inputs including jacket temperature and volumetric flow rate of initiator predict the appearance of multiplicity behavior as well as the saddle-node bifurcation points. The results, firstly, verify that the multiplicity behavior of the system appears under considered operating conditions. Secondly, the analysis of bifurcation behavior gives the theoretical prediction of multiplicity behavior once the operating conditions vary due to the soft constraints or the effect of noise and disturbance.

In this paper mathematical models are proposed to simulate the dynamic behaviour of the reduction of Cr(VI) with Fe(II) in a batch stirred tank. Two approaches have been adopted to represent systems in non-equilibrium and quasi-equilibrium conditions. The models derived reflect the changes of the relevant reactant species concentration based on the Oxidation Reduction Potential (ORP) of the system. An automated pilot plant has been designed and commissioned to carry out on-line/real time data acquisition and control for this Cr(VI) reduction process experimentally. Both the quasi-equilibrium and non-equilibrium models were validated experimentally. Simulated and on-line results indicate distinctive ORP profiles pattern for these two processes. The ORP profiles for non-equilibrium processes show a distinctive pattern which indicates the complete reduction of Cr(VI) in the batch sample.

The design of a fire-tube shell boiler consists of a bundle of tubes contained inside a shell. The heat transfer process from the combustion gas to the boiling water via the tube surface is extremely complicated as it involves combustion, convection, conduction, and boiling process. Severe boiling and evaporating processes take place outside the fire-tube shell boiler where the steam is generated. However, many processes such as the regeneration of absorbent for CO2 removal needs optimum steam consumption, which reflects its high costs for operation. Therefore, good control performance of steam pressure becomes important. This study is aimed at developing a control scheme to minimize the effect of over-firing on the fire-tube shell boiler which impacts on excessive fuel consumption. The Neural Network Predictive Controller (NNPC) is used in this work, using the optimization and neural toolboxes which are written in the MATLAB code and were compared with the PID controller for set-point tracking and disturbance rejection ability. The comparison reveals that NNPC gives an excellent alternative to PID controller due to the non-linearity of the fire-tube shell boiler system. Since NNPC can reduce the fuel consumption by minimizing actuator moves, the control of boiler and burners in this plant in Brunei is recommended to be upgraded to replace the existing PID controller.

The processes that take place in industrial reactors are mainly highly nonlinear and, due to this, their controls have become a major factor in determining the aspect of process safety, quality, and productivity of these systems. For this reason, we have designed an inexpensive, practical reactor on a pilot plant scale to conduct such online control studies where the heat of reaction in the reactor is simulated by injecting steam into the reactor system. This paper highlights the development of the pilot plant with its software development to implement advanced control strategies using artificial intelligence approaches such as fuzzy logic and genetic algorithm. The online implementation results conclude that the Genetic Algorithm Model Based Controller (GAMBC) gave similar performance with the Fuzzy Logic Controller (FLC) for the set point tracking studies but in the load disturbance rejection studies, it was found that the FLC performed better. The results obtained show the usefulness and importance of these experimental studies to further understand advanced control applications for a typical industrial chemical process such as a reactor.

The multizone circulating reactor (MZCR) technology is a newly announced reactor design for solid-catalyzed gas-phase polymerization. The process is characterized by two hydrodynamically distinct but interconnecting polymerization zones, thus forming a continuous loop of polymer flow between the said zones. In the present study, a dynamic model for the MZCR reactor is developed to illustrate the basic dynamic behavior of the new reactor design; the model is used to study the copolymerization of ethylene with butene. Several parameter sensitivity analyses are performed to show the computer-simulated time responses for reactor temperature, number-average molecular weight, weight-average molecular weight, catalyst feed rate and the monomer concentration along the reactor length.