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Fault detection and diagnosis (FDD) systems are developed to characterize normal variations and detect abnormal changes in a process plant. It is always important for early detection and diagnosis, especially in chemical process systems to prevent process disruptions, shutdowns, or even process failures. However, there have been only limited reviews of data-driven FDD methods published in the literature. Therefore, the aim of this review is to provide the state-of-the-art reference for chemical engineers and to promote the application of data-driven FDD methods in chemical process systems. In general, there are two different groups of data-driven FDD methods: the multivariate statistical analysis and the machine learning approaches, which are widely accepted and applied in various industrial processes, including chemicals, pharmaceuticals, and polymers. Many different multivariate statistical analysis methods have been proposed in the literature, such as principal component analysis, partial least squares, independent component analysis, and Fisher discriminant analysis, while the machine learning approaches include artificial neural networks, neuro-fuzzy methods, support vector machine, Gaussian mixture model, K-nearest neighbor, and Bayesian network. In the first part, this review intends to provide a comprehensive literature review on applications of data-driven methods in FDD systems for chemical process systems. In addition, the hybrid FDD frameworks have also been reviewed by discussing the distinct advantages and various constraints, with some applications as examples. However, the choice for the data-driven FDD methods is not a straightforward issue. Thus, in the second part, this paper provides a guideline for selecting the best possible data-driven method for FDD systems based on their faults. Finally, future directions of data-driven FDD methods are summarized with the intent to expand the use for the process monitoring community.

This is a detailed review on olefin polymerization models, and the most recent process control approaches used to control these nonlinear systems are presented. Great focus has been given to the various approaches of fluidized-bed reactor (FBR) modeling. Currently, there has yet to be a single model that blends these modeling aspects together into one single formulation. In this article, the classification of models works by looking at their assumption in considering the phases inside the system. Researchers have been unraveling vast information to narrate in detail the relations between various variables that can be found in FBRs. Although it is not difficult to understand about the basics of modeling polymer properties, a gap exists for future researchers to justify in detail the phenomena and reduce the gap between model predictions and the actual data. The various controlling approaches to control these FBRs have also been reviewed and categorized depending on the method they used to control significant parameters of this nonlinear system. The progress that can be expected in this field leads to the creation of more efficient reactors and minimizing waste.

This paper presents a metaheuristic-based temperature controller for an exothermic batch process. Developing a suitable temperature controller for an exothermic process is a challenging task because large amount of heat is released rapidly during the process. The exothermic behavior will further increase the reaction rate and cause more heat to be liberated. As the result, the improper temperature control might cause the reaction becomes unstable and consequently poses safety concern to the plant personnel and equipment. The conventional non-metaheuristic-based controller, such as fuzzy logic requires empirical data or knowledge about the total amount heat released while developing its fuzzy rules and membership functions for precision control. However, the detailed kinetic model of the heat released is unable to be obtained since there are several unobservable parameters during the process, such as the energy held in the reactor and jacket walls. Therefore, particle swarm optimization algorithm (PSO) is proposed as the controller to maintain the reactor temperature at the desired trajectory by manipulating the inlet jacket fluid temperature and flow rate. The simulation results show the proposed PSO produces better performances in terms of minimizing fluctuation in control actions and overshooting as compared with the conventional fuzzy logic controller.

This work proposes a tracking error passivity-based multivariable control viafeedback passivation for a class of free-radical polymerization systems in a continuouslystirred tank reactor (CSTR). Firstly, this system dynamics with high nonlinearity ispassivized by input coordinate transformations, the resulting passive system is then rewritten into a canonical form strongly related to the so-called Port Control Hamiltonianstructure. Actually, this representation allows to show the physical meanings of systemdynamics such as dissipative, non-dissipative terms and supply rate. From this, a feedbackcontroller based on tracking error is designed for the globally exponential stabilization atan arbitrarily chosen reference trajectory passing the desired equilibrium point. Thetheoretical developments are then illustrated for polystyrene polymerization system inthe CSTR. The numerical simulations show that the trajectories of styrene polymerizationsystem considered as an illustrative example of FRP system converges globallyexponentially to the imposed trajectories.

In an industrial fluidized bed polymerization reactor, particle size distribution (PSD) plays a significant role in the reactor efficiency evaluation. The computational fluid dynamic (CFD) models coupled with population balance model (CFD-PBM) have been extensively employed to highlight its potential to analyze the industrial-scale gas phase polymerization reactor (FBRs) utilizing ANSYS Fluent software. The predicted results reveal an acceptable agreement with the observed industrial data in terms of pressure drop and bed height. Courant number independent study has been carried out to record the mesh and time step independent results for large scale FBRs. Furthermore, the minimum fluidization velocity ( U mf ) and size-dependent particle growth rate have been assessed to emphasize the impact of PSD along the reactor. The results show transient regime in the case of minimum fluidization velocity. The simulation results signify that in order to improve the polymerization yield, the amount of gas velocity can be increased without change in the fluidization regime, i.e. segregation. Hence, the 2D CFD-PBM/DQMOM coupled model can be used as a reliable tool for analyzing and improving the design and operation of the gas phase polymerization FBRs.

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.

This literature survey focuses on the application of computational fluid dynamics (CFD) in various aspects of the fluidized bed reactor. Although fluidized bed reactors are used in various industrial applications, this first-of-its-kind review highlights the use of CFD on polyolefin production. It is shown that CFD has been utilized for the following mechanisms of polymerization: governing of bubble formation, electrostatic charge effect, gas–solid flow behavior, particle distribution, solid–gas circulation pattern, bed expansion consequence, mixing and segregation, agglomeration and shear forces. Heat and mass transfer in the reactor modeling using CFD principles has also been taken under consideration. A number of softwares are available to interpret the data of the CFD simulation but only few softwares possess the analytical capability to interpret the complex flow behavior of fluidization. In this review, the popular softwares with their framework and application have been discussed. The advantages and feasibility of applying CFD to olefin polymerization in fluidized beds were deliberated and the prospect of future CFD applications was also discussed.

In SOFC (solid oxide fuel cell) systems operating at high temperatures, temperature fluctuation induces a thermal stress in the electrodes and electrolyte ceramics; therefore, the cell temperature distribution is recommended to be kept as constant as possible. In the present work, a mathematical model based on first principles is presented to avert such temperature fluctuations. The fuel cell running on ammonia is divided into five subsystems and factors such as mass/energy/momentum transfer, diffusion through porous media, electrochemical reactions, and polarization losses inside the subsystems are presented. Dynamic cell-tube temperature responses of the cell to step changes in conditions of the feed streams is investigated. The results of simulation indicate that the transient response of the SOFC is mainly influenced by the temperature dynamics. It is also shown that the inlet stream temperatures are associated with the highest long term start-up time (467 s) among other parameters in terms of step changes. In contrast the step change in fuel velocity has the lowest influence on the start-up time (about 190 s from initial steady state to the new steady state) among other parameters. A NNPC (neural network predictive controller) is then implemented for thermal stress management by controlling the cell tube temperature to avoid performance degradation by manipulating the temperature of the inlet air stream. The regulatory performance of the NNPC is compared with a PI (proportional–integral) controller. The performance of the control system confirms that NNPC is a non-linear-model-based strategy which can assure less oscillating control responses with shorter settling times in comparison to the PI controller.

The modified sinusoidal pulse-width modulation (SPWM) is one of the PWM techniques used in three-phase AC–DC buck converters. The modified SPWM works without the current sensor (the converter is current sensorless), improves production of sinusoidal AC current, enables obtainment of near-unity power factor, and controls output voltage through modulation gain (ranging from 0 to 1). The main problem of the modified SPWM is the huge starting current and voltage (during transient) that results from a large step change from the reference voltage. When the load changes, the output voltage significantly drops (through switching losses and non-ideal converter elements). The single-input single-output (SISO) approach with minor-loop voltage feedback controller presented here overcomes this problem. This approach is created on a theoretical linear model and verified by discrete-model simulation on MATLAB/Simulink. The capability and effectiveness of the SISO approach in compensating start-up current/voltage and in achieving zero steady-state error were tested for transient cases with step-changed load and step-changed reference voltage for linear and non-linear loads. Tests were done to analyze the transient performance against various controller gains. An experiment prototype was also developed for verification.

A two-phase dynamic model, describing gas phase propylene polymerization in a fluidized bed reactor, was used to explore the dynamic behavior and process control of the polypropylene production rate and reactor temperature. The open loop analysis revealed the nonlinear behavior of the polypropylene fluidized bed reactor, justifying the use of an advanced control algorithm for efficient control of the process variables. In this case, a centralized model predictive control (MPC) technique was implemented to control the polypropylene production rate and reactor temperature by manipulating the catalyst feed rate and cooling water flow rate respectively. The corresponding MPC controller was able to track changes in the setpoint smoothly for the reactor temperature and production rate while the setpoint tracking of the conventional proportional-integral (PI) controller was oscillatory with overshoots and obvious interaction between the reactor temperature and production rate loops. The MPC was able to produce controller moves which not only were well within the specified input constraints for both control variables, but also non-aggressive and sufficiently smooth for practical implementations. Furthermore, the closed loop dynamic simulations indicated that the speed of rejecting the process disturbances for the MPC controller were also acceptable for both controlled variables.

Production of fructose from glucose isomerisation process using commercial immobilized glucose isomerase (IGI) was conducted in a batch type of stirred tank bioreactor. A mathematical model was developed to describe the effect of temperature and pH on the kinetic parameters of fructose production. Modified Santos model known as MM3 was used to describe this phenomenon. The influence of temperature and pH was investigated and quantified. The results showed that, even though the highest R 2 was at 70°C but based on the IAE of 0.843 and ISE of 0.978, it proved that for enzymatic reaction, it should be carried out below 65°C. Effect of pH for various models have shown that, the IAE and ISE were less than one whereas the R 2 were greater than 0.95. This indicates that the model, MM3 is acceptable. Keywords: Batch reactor, fructose, glucose isomerisation, mathematical modeling, pH, temperature

a b s t r a c t The control of a gas phase propylene polymerization model in a fluidized bed reactor was studied, where the rigorous two phase dynamic model takes into account the polymerization reactions occurring in the bubble and emulsion phases. Due to the nonlinearity of the process, the employment of an advanced control scheme for efficient regulation of the process variables is justified. In this case, the Adaptive Pre- dictive Model-Based Control (APMBC) strategy (an integration of the Recursive Least Squares algorithm, RLS and the Generalized Predictive Control algorithm, GPC) was employed to control the polypropylene production rate and emulsion phase temperature by manipulating the catalyst feed rate and reactor cool- ing water flow, respectively. Closed loop simulations revealed the superiority of the APMBC in setpoint tracking as compared to the conventional PI controllers tuned using the Internal Model Control (IMC) method and the standard Ziegler-Nichols (Z-N) method. Moreover, the APMBC was able to efficiently arrest the effects of superficial gas velocity, hydrogen concentration and monomer concentration on the process variables, thus exhibiting excellent regulatory control properties.

A neuro-fuzzy modeling technique was used to predict the effective of thermal conductivity of various fruits and vegetables. A total of 676 data point was used to develop the neuro-fuzzy model considering the inputs as the fraction of water content, temperature and apparent porosity of food materials. The complexity of the data set which incorporates wide ranges of temperature (including those below freezing points) made it difficult for the data to be predicted by normal analytical and conventional models. However the adaptive neuro-fuzzy model (ANFIS) was able to predict conductivity values which closely matched the experimental values by providing lowest mean square error compared to multivariable regression and conventional artificial neural network (ANN) models. This method also alleviates the problem of determining the hidden structure of the neural network layer by trial and error.

In this paper, the development and implementation of a novel semiactive suspension control of a quarter-car model using a hybrid-fuzzy-logic-based controller have been done. The proposed quarter-car model can be described as a nonlinear two-degree-of-freedom system, which is subject to system disturbances from different road profiles. In order to implement the suspension system experimentally, the magnetorheological (MR) fluid has been used as an adjustable damper. The MR damper is a control device that consists of a hydraulic cylinder filled with magnetically polarizable particles suspended in a liquid. The MR damper rapidly dissipates vibration by absorbing energy. In this paper, proportional-integral-derivative (PID), fuzzy logic, and hybrid controllers are used to control the semiactive car suspension system. The results show that both fuzzy logic and hybrid controllers are quite suitable to eliminate road disturbances for the semiactive suspension system considerably as compared to the conventional PID controller.

A comprehensive mechanistic model describing gas-phase propylene polymerization is developed. The kinetics of polymerization is based on a multiple active site for Ziegler–Natta catalyst. The model considers the polymerization reaction to take place in both bubble and emulsion phases. The developed model was used to predict polymer production rate, number and weight average molecular weights, polydispersity index (PDI) and melt flow index (MFI). Results showed that by increasing the superficial gas velocity from 0.1 to 0.7 m/s the proportion of the polymer produced in the bubble phase increases from 7.92% to 13.14% which highlights the importance of considering the existence of catalyst in the bubble phase. Comparing the developed model with published models of the same reactor revealed that the polymer productivity will be higher using the new model at high catalyst feed rate.

Producing biodiesel from palm oil as a raw material involves complex transesterification reactions which add up to the process nonlinearity. In this work, more emphasis will be focused on the react...