Fuzzy Control Of A Fluidized Bed Dryer

Fluidized bed dryers are utilised in almost every area of drying applications and therefore improved control strategies are always of great interest. The nonlinear character of the process, exhibited in the mathematical model and the open loop analysis, implies that a fuzzy logic controller is appropriate because, in contrast with conventional control schemes, fuzzy control inherently compensates for-process nonlinearities and exhibits more robust behaviour. In this study, a fuzzy logic controller is proposed; its design is based on a heuristic approach and its performance is compared against a conventional PI controller for a variety of responses. It is shown that the fuzzy controller exhibits a remarkable dynamic behaviour, equivalent if not better than the PI controller, for a wide range of disturbances. In addition, the proposed fuzzy controller seems to be less sensitive to the nonlinearities of the process, achieves energy savings and enables MIMO control.     

ADVANCED CONTROL STRATEGIES FOR FLUIDIZED BED DRYERS

ABSTRACT

Generating the best possible control strategy comprises a necessity for industrial processes, by virtue of product quality, cost reduction and design simplicity. Three different control approaches, namely an Input-Output linearizing, a fuzzy logic and a PID controller, are evaluated for the control of a fluidized bed dryer, a typical non-linear drying process of wide applicability. Based on several closed loop characteristics such as settling times, maximum overshoots and dynamic performance criteria such as IAE, ISE and ITAE, it is shown that the Input-Ouput linearizing and the fuzzy logic controller exhibit a better performance compared to the PID controller tuned optimally with respect to the IAE, for a wide range of disturbances; yet, the relevant advantage of the fuzzy logic over the conventional nonlinear controller issues upon its design simplicity. Typical load rejection and set-point tracking examples are given to illustrate the effectiveness of the proposed approach.     

Adaptive type-2 fuzzy logic control of a bioreactor

Two adaptive type-2 fuzzy logic controllers with minimum number of rules are developed and compared by simulation for control of a bioreactor in which aerobic alcoholic fermentation for the growth of Saccharomyces cerevisiae takes place. The bioreactor model is characterized by nonlinearity and parameter uncertainty. The first adaptive fuzzy controller is a type-2 fuzzy-neuro-predictive controller (T2FNPC) that combines the capability of type-2 fuzzy logic to handle uncertainties, with the ability of predictive control to predict future plant performance making use of a neural network model of the nonlinear system. The second adaptive fuzzy controller is instead a self-tuning type-2 PI controller, where the output scaling factor is adjusted online by fuzzy rules according to the current trend of the controlled process. The performance of a type-2 fuzzy logic controller with 49 rules is used as reference.     

基于DSP的pH过程FNNC-PI控制器研究

提出一种模糊神经网络控制与传统PI相结合的控制方法,即pH过程的FNNC PI控制方案,将模糊控制具有的较强逻辑推理功能、神经网络的自学习能力以及传统PI控制的优点融为一体,能很好地处理pH过程的非线性和滞后性,具有较强的鲁棒性和抗干扰能力。为了满足控制运算实时性的要求,采用TMS320VC33高速数字信号处理器(DSP)作为控制与运算单元,成功地完成了模糊神经网络控制器的DSP实现。     

Advantage of Low‐Cost Predictive Control: Study Case on a Train of Distillation Columns

The process of enriching the ¹³C isotope, performed in trains of cryogenic distillation columns, exhibits large settling times, nonlinearities, large dead‐times, and are difficult to model precisely. Such equipment has been developed in Romania, with concentration increasing up to 70 %. A control analysis for a single unit has already been done including a decentralized multivariable PI controller and two decoupling control algorithms based on the internal model control (IMC) approach. Here, a multivariable predictive controller, the extended prediction self‐adaptive controller is proposed. The simulation results, considering significant modeling errors, demonstrate that this represents a more suitable choice than the previously designed strategies. Comparisons are included to support this idea.     

FUZZY GAIN SCHEDULED CSTR WITH GA-BASED PID

In this article, a new control scheme, the gain scheduled genetic algorithm (GA)-based PID is proposed for a continuous stirred tank reactor (CSTR). A CSTR is a highly nonlinear process that exhibits stability in certain regions and instability in other regions. The proposed control scheme implements the characteristics of the genetic algorithm's (GA) global optimization to optimize the PID's three control parameters, k_p, k_i, k_d, to obtain the best control effect by minimizing the integral square error online. The PID controller parameters tuned by the GA for each region are gain scheduled by a fuzzy logic scheduler. Fuzzy gain scheduling is a special form of fuzzy control that uses linguistic rules and fuzzy reasoning to determine the controller parameter transition policy for the dynamic plant subject to large changes in its operating state. Simulation results show the feasibility of using the proposed controller for the control of the dynamical nonlinear CSTR.     

ARTIFICIAL NEURAL NETWORKS: PRINCIPLE AND APPLICATION TO MODEL BASED CONTROL OF DRYING SYSTEMS - A REVIEW

Abstract

This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with upto-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN     

ADVANCED CONTROL STRATEGIES FOR FLUIDIZED BED DRYERS

Generating the best possible control strategy comprises a necessity for industrial processes, by virtue of product quality, cost reduction and design simplicity. Three different control approaches, namely an Input-Output linearizing, a fuzzy logic and a PID controller, are evaluated for the control of a fluidized bed dryer, a typical non-linear drying process of wide applicability. Based on several closed loop characteristics such as settling times, maximum overshoots and dynamic performance criteria such as IAE, ISE and ITAE, it is shown that the Input-Ouput linearizing and the fuzzy logic controller exhibit a better performance compared to the PID controller tuned optimally with respect to the IAE, for a wide range of disturbances; yet, the relevant advantage of the fuzzy logic over the conventional nonlinear controller issues upon its design simplicity. Typical load rejection and set-point tracking examples are given to illustrate the effectiveness of the proposed approach.     

Optimization‐Based Nonlinear Centralized Controller Tuning of Liquid‐Liquid Extraction Processes

Abstract

The control problem of an agitated contactor is considered in this work. A Scheibel extraction column is modeled using the non‐equilibrium backflow mixing cell model. Model dynamic analysis shows that this process is highly nonlinear, thus the control problem solution of such a system needs to tackle the process nonlinearity efficiently. The control problem of this process is solved by developing a multivariable nonlinear control system implemented in MATLAB?. In this control methodology, a new controller tuning method is adopted, in which the time‐domain control parameter‐tuning problem is solved as a constrained optimization problem. A MIMO (multi‐input multi‐output) PI controller structure is used in this strategy. The centralized controller uses a 2×2 transfer function and accounts for loops interaction. The controller parameters are tuned using an optimization‐based algorithm with constraints imposed on the process variables reference trajectories. Incremental tuning procedure is performed until the extractor output variables transient response satisfies a preset uncertainty which bounds around the reference trajectory. A decentralized model‐based IMC (internal model control) control strategy is compared with the newly developed centralized MIMO PI control one. Stability and robustness tests are applied to the two algorithms. The performance of the MIMO PI controller is found to be superior to that of the conventional IMC controller in terms of stability, robustness, loops interaction handling, and step‐change tracking characteristics.     

Dynamic Modelling and Process Control of ZnS Precipitation

Abstract

This paper presents the dynamic modelling and design of a control strategy for the ZnS precipitation process. During lab‐scale experiments, the sulfide concentration in a precipitator was controlled at a prespecified pS value by manipulating the flow from a buffer vessel. Batch tests showed that the optimal condition for zinc sulfide precipitation is at a sulfide concentration of 10^?15 mole/l (pS 15). Experiments with the precipitator showed that the sulfide concentration highly deviates from a given setpoint when proportional (P) control is used, but this deviation can be decreased using a Proportional Integral (PI) controller. Moreover, the PI controller was able to handle sudden disturbances in the process conditions (pH, influent flow rate, or zinc and sulfide concentration). Additional precipitation experiments were conducted using effluent from a sulfate reducing gas‐lift reactor to determine if the compounds present in the effluent influence the control process. With the gas‐lift reactor effluent and a PI controller, the desired sulfide concentration was reached almost instantaneously (within 15 minutes) within acceptable margins (2–5%).     

模糊自适应PI控制在空调系统中的研究

以空气焓差法试验台空调系统的温度控制系统为具体仿真对象建立了数学模型,该空调系统可以看作是一阶惯性加纯滞后的环节,而且对象的过程参数和时延时间是时变的,传统的PID控制无法获得理想的控制效果。提出了一种无需辨识环节的具有智能的模糊自适应PI的控制算法并将其应用在该空调系统中,该算法对模糊控制和PI控制进行有机结合,根据实际控制经验,通过模糊控制规则对控制回路中PI控制器的参数进行实时整定,并将该控制算法和经过良好整定的PI控制器在空调系统中的控制性能进行比较。仿真结果表明,模糊自适应PI控制提高了系统的鲁棒性、减小了超调量、提高了抗干扰能力、缩短了调整时间。     

DYNAMIC FUZZY ADAPTIVE CONTROLLER FOR pH

Control of pH processes is very difficult due to nonlinear dynamics, high sensitivity at the neutral point, and changes in the concentrations of known or unknown chemical species. In this study, a dynamic fuzzy adaptive controller (DFAC) with a new inference mechanism is proposed and applied for the control of pH processes. The DFAC consists of a low-level basic control phase with a minimum rule base and a high-level dynamic learining phase with an updating mechanism to interact and modify the control rule base. The DFAC can self-adjust its fuzzy control rules using information from the process during on-line control and create new fuzzy control rules or modify the present control rules using its learning capability from past control trends. The controller is evaluated by applying it to a weak acid-strong base pH process with input disturbances and to another pH process that involve that has changes in acidic/buffering streams. The results of the DFAC with the new inference mechanism are compared with the known inference mechanisms, the fuzzy controller, the conventional PI controller, and also with an adaptive PID controller. The proposed DFAC provides better performance for set point tracking of the pH and rejection of load disturbances and buffering affects.     

MODELLING AND CONTROL OF AN ALFALFA ROTARY DRYER

ABSTRACT

A dynamic model of an alfalfa rotary dryer was developed and used to test the performance of two different feedback controllers. One controller is a conventional PI (Proportional-Integral) controller with fixed tuning parameters whereas the other is a gain-scheduled PI controller with automatically adjusted tuning parameters. The performance of the two controllers was compared with the performance of the dryer under manual control. The gain-scheduled PI controller was found to be superior in the sense that it used less control action and achieved the same control performance as the fixed tuning parameter PI controller. The use of the gain-scheduled controller was shown to reduce energy consumption, increase dryer throughput and had an estimated pay-back time of nine months.     

Fuzzy logic control of an unstable biological reactor

In the present work, we employ a fuzzy logic controller (FLC) to control the unstable state of a nonlinear biological reaction. The state variable vectors consist of cell density and substrate concentration. The dilution rate is used as a manipulated variable to control the reaction dynamics. An analytic form of FLC employing Zadeh AND logic along with Center of Mass defuzzification method is considered. Simulations reveal that for servo response test, the FLC shows satisfactory performance for natural unsteady states for which a conventional PI controller is known to fail. Further simulations also show that the FLC gives satisfactory regulatory response and is relatively insensitive to the deviations in model parameters.     

Control of injection velocity using a fuzzy logic rule-based controller for thermoplastics injection molding

Injection velocity to a large degree determines the melt injection rate during the injection phase, and it has critical impact on the molded part quality, such as shrinkage, warpage, and impact strength. An injection molding machine operates with different injection velocity profiles, barrel temperatures, molds, and materials. These strongly different molding conditions cause the injection velocity dynamics to very significantly and to make the control performance of the injection velocity poor with a typical PID controller. A real-time, closed-loop feedback and feedforward control system based on fuzzy logic has been designed, developed, and implemented to control the injection velocity. The fuzzy logic rules of the controller are optimized by analyzing phase plane characteristics. The controller output membership functions are optimized based on a 2^k factorial design technique. The experimental results reveal that the fuzzy logic-based controller works well with different molds, materials, barrel temperatures, and injection velocity profiles, indicating that the fuzzy logic controller has superior performance over the conventional PID controller in response speed, set-point tracking ability, noise rejection, and robustness.     

Application of genetic algorithm to self-organizing fuzzy controller in fed-batch culture ofScutellaria baicalensis G. plant cell

A self-organizing fuzzy controller is constructed for control of substrate concentration in fed-batch operation of a cell culture process. A genetic algorithm is used to generate fuzzy rules of the self-organizing fuzzy controller and to modify the universe of discourse automatically. The fuzzy controller is designed for the application ofScutellaria baicalensis G. plant cell culture process as a model system. A substrate feeding strategy of a two stage culture method to maximize flavone glycoside production in fed-batch culture ofScutellaria baicalensis G. plant cell is proposed based on structured model of growth and product synthesis. As a two stage culture, the feeding strategy consists of the first period at 22 g/L of glucose concentration to promote cell growth and the second period at 10 g/L of glucose concentration to promote flavone glycoside synthesis. The designed self-organizing fuzzy controller is applied to regulate the glucose concentration at a given set-point to increase flavone glycoside synthesis. The simulation results show that the proposed feeding strategy in a fed-batch culture enhances flavone glycoside production and the self-organizing fuzzy logic controller generated by genetic algorithm improves controller performance.     

模糊非线性内模控制算法及其在pH值控制中的应用

pH值控制过程具有较强的非线性,历来是过程控制研究的一大热点,本文针对pH值控制系统提出了一种基于模糊推理网的非线性内模控制算法（FNIMC）。模糊推理网用于辨识对象的模糊模型;FNIMC由一个逆模控制器和具有一个可调参数的鲁棒滤波器组成。仿真结果表明该算法优于非线性PID调节器,且计算效率高。     

On-Farm Drying and Storage Systems

ABSTRACT

Rotary dryers are widely used for the continuous drying of minerals and chemicals on a large scale. Hot gases are passed parallel to the flowing solid to achieve the desired product moisture content. Because these dryers are energy intensive, it is mandatory to operate them as efficiently as possible to respond to economic pressures. Using a dynamic rotary dryer simulator for mineral concentrate, five control strategies are evaluated and compared. Two control strategies are based on PI controllers and the others use neural network models. Results clearly show that a feedforward action, in conjunction with a PI controller or incorporated within the structure of a neural network model, led to the best performances provided an accurate measurement of the feed moisture content is available.     

区间二型模糊免疫PID在环己烷无催化氧化温度控制系统中的应用

环己烷无催化氧化过程具有非线性、多变量耦合、大时滞等特点,使用常规比例积分微分(PID)控制方案无法达到理想的控制性能。提出了一种区间二型模糊免疫PID控制器,其本质上是一种基于免疫PID的非线性控制器,利用区间二型模糊逻辑系统来逼近免疫反馈律中的非线性函数,以提升控制器处理和逼近复杂不确定非线性系统的能力。将所提出的控制器应用于环己烷无催化氧化温度控制系统,仿真结果表明该方法是有效的。     

Integrating data‐based modeling and nonlinear control tools for batch process control

A data‐based multimodel approach is developed in this work for modeling batch systems in which multiple local linear models are identified using latent variable regression and combined using an appropriate weighting function that arises from fuzzy c‐means clustering. The resulting model is used to generate empirical reverse‐time reachability regions (RTRRs) (defined as the set of states from where the data‐based model can be driven inside a desired end‐point neighborhood of the system), which are subsequently incorporated in a predictive control design. Simulation results of a fed‐batch reactor system under proportional‐integral (PI) control and the proposed RTRR‐based design demonstrate the superior performance of the RTRR‐based design in both a fault‐free and faulty environment. The data‐based modeling methodology is then applied on a nylon‐6,6 batch polymerization process to design a trajectory tracking predictive controller. Closed‐loop simulation results illustrate the superior tracking performance of the proposed predictive controller over PI control. © 2011 American Institute of Chemical Engineers AIChE J, 2012