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Application to Industrial Processes
Abstract
In this chapter, chemical industrial processes are considered, and Linear Algebra-Based Control Design (LAB CD) is the approach used to design the controller. First, the case of dealing with a nonlinear first principles–based model of the process is considered. Then, an experimental linearized model around an operating point is considered, and, again, the LAB CD methodology is applied to design the control. The simple model based on a first-order plus time delay (FOPTD) transfer function is used, and the controlled plant behavior is shown to be appropriate for small changes in the reference. A gain-scheduling adaptation scheme is suggested for larger reference changes. Thence, a design applicable to a large variety of processes is obtained. In order to better illustrate the procedure, the control design for a typical continuous stirred tank reactor (CSTR) is developed.
... CTA exhibited high crystallinity, hydrophobicity, and excellent chemical, mechanical, and thermal resistance [6,7]. CTA was synthesized using a chemical reaction in acetic acid solvent between all groups of hydroxyl cellulose and carboxylic acid anhydride catalyzed by sulfuric acid [8,9]. CTA has been synthesized from a variety of primary sources, including recycled newspapers, cotton fibers, ramie fibers, empty palm oil waste, date palm seeds and eucalyptus pulp [10][11][12][13]. ...
As a promising biodegradable polymer, cellulose triacetate (CTA) was synthesized and plasticized with ionic liquids to produce flexible biocomposite films multi-end-use. Initially, CTA polymer was synthesized from industrial waste cotton using a heterogeneous pathway. Subsequently, four ionic liquids, namely 3-hexyl-1-methyl imidazolium acetate [C6MIM][Ac], 3-hexyl-1-methyl imidazolium hexafluorophosphate [C6MIM][PF6], N-hexyl N,N,N-triethylammonium acetate [N2226][Ac] and N-hexyl N,N,N-triethylammonium hexafluorophosphate [N2226][PF6] were synthesized and used as plasticizer materials of CTA film. Indeed, the films were prepared at room temperature using the solution-casting method, and the effects of ionic liquids on their morphological, mechanical, and thermal properties were evaluated. Morphological results indicated that the prepared films had smooth surfaces and high transparency. The addition of ionic liquids significantly reduced the crystallinity of CTA film and made it ductile. The Mechanical and thermal results showed that the ionic liquid anion has a significant effect on the properties of the film. CTA films plasticized with ionic liquids based on [PF6]⁻ were more flexible than the others, with an elongation of 18.22% instead of 1.97%. The climate ageing test, on the other hand, confirmed that the used ILs could indeed maintain the plasticized state during aging with a better stability of the hydrophobic IL in the polymer matrix compared to the hydrophilic IL. There are self-extinguishing with a final residue of about 97 wt%, allowing CTA films to be used for thermal insulation and fire protection, as well as packaging applications.
Graphical Abstract
In this paper a controller is proposed based on linear algebra for a fed-batch bioethanol production process. It involves finding feed rate profiles (control actions obtained as a solution of a linear equations system) in order to make the system follow predefined concentration profiles. A neural network states estimation is designed in order to know those variables that cannot be measured. The controller is tuned using a Monte Carlo experiment for which a cost function that penalizes tracking errors is defined. Moreover, several tests (adding parametric uncertainty and perturbations in the control action) are carried out so as to evaluate the controller performance. A comparison with another controller is made. The demonstration of the error convergence, as well as the stability analysis of the neural network, are included.
A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent "time" variables, namely, the run time during a batch and the batch index. Accordingly, the control and optimization objectives can be defined for a given batch or over several batches. This chapter describes the various control and optimization strategies available for the operation of batch processes. These include online and run-to-run control on the one hand, and repeated numerical optimization and optimizing control on the other. Several case studies are presented to illustrate the various approaches.
In order to enhance the yield and productivity of metabolite production, researchers have focused almost exclusively on enzyme amplification or other modifications of the product pathway. However, overproduction of many metabolites requires significant redirection of flux distributions in the primary metabolism, which may not readily occur following product deregulation because metabolic pathways have evolved to exhibit control architectures that resist flux alterations at branch points. This problem can be addressed through the use of some general concepts of metabolic rigidity, which include a means for identifying and removing rigid branch points within an experimental framework.
This work presents a simple controller, tunable by 3 parameters, capable of following variable time references. It is a Linear Algebra based Controller (LABC), developed from a First Order Plus Dead Time model (FOPDT). Two processes were selected: a high order linear process and a transesterification batch reactor from the biodiesel production process. Constant and variable time references were followed with very low tracking error. The methodology for the controller design under different conditions is described and results of simulations and experimental assays on a batch reactor are shown. Through comparison against PID and a Numerical Method Based Controller presented by other researchers, the accuracy of LABC is evidenced.
This paper aims to solve the problem of tracking optimal profiles for a nonlinear multivariable fed-batch bioprocess by a simple but efficient closed-loop control technique based on a linear algebra approach. In the proposed methodology, the control actions are obtained by solving a system of linear equations without the need for state transformations. The optimal profiles to follow are directly those corresponding to output desired variables, therefore, estimation of states for nonmeasurable variables is considered by employing a neural networks method. The efficiency of the proposed controller is tested through several simulations, including process disturbances and operation under parametric uncertainty. The optimal controller parameters are selected through the Montecarlo Randomized Algorithm. In addition, proof of convergence to zero of tracking errors is analyzed and included in this article.
Based on a linear algebra approach, this paper aims at developing a novel control law able to track reference profiles that were previously-determined in the literature. A main advantage of the proposed strategy is that the control actions are obtained by solving a system of linear equations. The optimal controller parameters are selected through Monte Carlo Randomized Algorithm in order to minimize a proposed cost index. The controller performance is evaluated through several tests, and compared with other controller reported in the literature. Finally, a Monte Carlo Randomized Algorithm is conducted to assess the performance of the proposed controller.
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Step and relay feedback tests have been widely used for model identification in the process industry. The corresponding identification methods developed in the past three decades are surveyed in this paper. Firstly, the process models with time delay mainly adopted for identification in the literature are presented with a classification on different response types. By categorizing the major technical routes developed in the existing references for parameter estimation relating to different applications, the identification methods are subsequently clustered into groups for overview, along with two specific categories for robust identification against load disturbance and the identification of multivariable or nonlinear processes. The rationales of each category are briefly explained, while a typical or state-of-the-art identification algorithm of each category is elucidated along with application to benchmark examples from the literature for illustrating the achievable accuracy and robustness, so as to facilitate the readers to have a general knowledge of the research development. Finally, an outlook on the open issues regarding step or relay identification is provided to call attention to future exploration.
In this paper, we present an introduction to Monte Carlo and Las Vegas randomized algorithms for systems and control. Specific applications of these algorithms include stability analysis, Lyapunov functions, and distributed consensus problems.
Some strongly nonlinear processes, such as chemical reactors, have sufficiently perverse dynamic behaviour in the open loop that even single input/single output control system design can be very difficult. In this paper, examples of this nonlinear behaviour are presented and some new methods of dynamic analysis discussed. Control system design problems, such as parametric sensitivity, ignition/extinction phenomena, and nonlinear oscillations, are illustrated by example. Some outstanding research problems are discussed.
A new approach for the design of sliding mode controllers based on a first-order-plus-deadtime model of the process, is developed. This approach results in a fixed structure controller with a set of tuning equations as a function of the characteristic parameters of the model. The controller performance is judged by simulations on two nonlinear chemical processes.