Süleyman Karacan

Application of optimal adaptive generalized predictive control to a packed distillation column

Abstract In this work, optimal operating conditions for a packed distillation column and optimal adaptive generalized predictive control (OA-GPC) were investigated. Thus, the dynamic and steady-state properties of the packed distillation column distilling methanol–water mixture were observed experimentally and theoretically. Mathematical models for the packed distillation column were solved with orthogonal collocation on finite elements. Optimal operating conditions of the system were found by using Box–Wilson optimization method and “Experimental Design” technique. Two types of control algorithm were utilized for controlling the packed distillation column, viz. conventional proportional integral derivative (PID) and generalized predictive control (GPC) at optimal operating conditions. Overhead temperature control was examined experimentally and theoretically. Pseudo random binary sequence (PRBS) signal and recursive identification algorithm were used to estimate the relevant parameters of the polynomial ARIMAX model. Generally theoretical and experimental control results were in accord with each other and it was observed that OA-GPC produced better performance than PID for the packed distillation column.

Generalized Predictive Control to a packed distillation column for regulatory problems

Abstract In this work, adaptive Generalized Predictive Control (GPC) was investigated at the optimal operating conditions for a pilot plant binary packed distillation column. The studieswere made experimentally and theoretically. The dynamic behavior of the distillation column has been simulated using backmixing model and solved by utilizing Hermite Polynomials within the finite element procedure. The control of the overhead product temperature was examined for both experimental and theoretical works. Perturbations in feed composition were utilized as the disturbance and the reboiler heat duty was selected as the manipulated variable. Pseudo Random Binary Sequence (PRBS) signal and Bierman algorithm were used to estimate the relevant parameters of the system model for GPC. Generally theoretical and experimental control results were in good agreement with each other.

Application of a non-linear long range predictive control to a packed distillation column

Abstract In this paper, nonlinear long range predictive control was applied to a pilot packed distillation column separating a mixture of methanol–water. The use of polynomial nonlinear autoregressive integrated moving average with external input (NARIMAX) model related with top product temperature and reflux ratio for nonlinear control was emphasised. The first part of the paper presents a rigorous dynamic model based on fundamental chemical engineering principles formulated for a packed distillation column. The actual column response to step changes in the feed mole fraction and temperature agreed well with dynamic model predictions. Then, recursive Gauss–Newton prediction error algorithm was used to determine NARIMAX model parameters. This algorithm can be used efficiently for this model and a transfer function model relating the manipulated variable (reflux ratio) to the controlled variable (top product temperature) was obtained. The role of data prefiltering prior to model parameter estimation is examined to overcome the parameter bias problem caused by disturbances was demonstrated. Next, non-linear long range predictive control algorithm was successfully applied in controlling the packed distillation column.

The steady-state and dynamic analysis of packed distillation column based on partial differential approach

The steady-state and dynamic behavior of a binary packed distillation column has been simulated using the two film back-mixing model. The model solution has been obtained employing orthogonal collocation on finite elements. The approach using the Legendre or Jacobi polynomial has been tested on the solution of related models. A pilot plant scaled packed distillation column distilling methanol-water mixture was used for experimental work. The variation of overhead and bottom temperatures have been monitored by an on-line computer control system. A number of comparisons have been made between the results predicted in this work from back mixing model based on the two film theory and those obtained experimentally. In most cases the prediction of this work gave results which were closer to experiment than other numerical solutions, showing that Legendre polynomials for orthogonal collocation on finite element can be applied effectively to simulate the packed distillation column by using a partial differential approach.

Parametric and nonparametric model based control of a packed distillation column

Abstract Parametric and nonparametric model based control systems were applied to control the overhead temperature of a packed distillation column separating methanol–water mixture. Experimental and theoretical studies have been done to observe the efficiency and performance of both control systems. Generalized predictive control (GPC) system based on a parametric model has been tried to keep the overhead temperature at the desired set point. First, a parametric model which is controlled auto regressive integrated moving average (CARIMA) was developed and then the parameters of this model were identified by applying pseudo random binary sequence (PRBS) and using Bierman algorithm. After that this model was used to design the GPC system. Tuning parameters of the GPC system have been calculated using the simulation program of the packed distillation column. Using the predicted parameters, experimental and theoretical GPC systems were found very effective in controlling the overhead temperature. Dynamic matrix control (DMC) system based on a nonparametric model has been used to track the overhead temperature of the packed distillation column. For this purpose, a nonparametric model known as the dynamic matrix was determined using the reaction curve method. A step change in heat input to the reboiler was applied to the manipulated variable and the temperature of the overhead product was observed. After that, the dynamic matrix was used to design the DMC system. Several calculations have been done to define the DMC control parameters. The best values of the tuning parameter were used to realize the DMC system for controlling the overhead temperature experimentally and theoretically. In the presence of some disturbances, the DMC system gives oscillation and offset in experimental studies.

Application of multivariable generalized predictive control to a packed distillation column

Abstract The present work deals with the application of Multivariate Generalized Predictive Control (MGPC) systems to a packed distillation column. The steady-state and dynamic behaviour of the system have been simulated using two film plug flow model. The model solutions have been obtained employing orthogonal collocation on finite element. The Jacobi polynomials within the finite element procedure was tested to determine the phase flow rates, the liquid and vapour composition profiles and the temperature profiles. All the theoretical results were compared with experimental data obtained from a pilot-plant packed distillation column distilling methanol-water mixture. Decoupling and MGPC control of overhead and/or bottom compositions were examined. Perturbation in feed composition and, reflux ratio and the reboiler heat duty were utilized as the disturbance and the manipulated variables respectively. Performance of these systems was tested by using an integral square of error (ISE and IAE) criterion.

Pole placement self tuning control for packed distillation column

Abstract In this paper, we present results from the successful application of pole placement self-tuning control for a packed distillation column in a pilot plant. The steady-state and dynamic behaviour of a binary packed distillation column has been simulated using a back mixing model. The model solution has been obtained employing orthogonal collocation on finite elements. A number of comparisons are made with results obtained both theoretically and experimentally. After a brief description of the pole placement self tuning algorithm the results are compared for the application at a SISO plant. A pseudo-random binary sequence and Bierman algorithm are used to estimate the relevant parameters of the system model. Pole-placement technique is applied to self-tuning proportional-integral-derivative (PID) control. Both experimental and theoretical works were carried out and results were compared.

Application of optimal adaptive control based on generalized minimum variance to a packed distillation column

Abstract In this work, we present results from the successful application of optimal adaptive control with Generalized Minimum Variance method for packed distillation column in a pilot plant. The steady-state and dynamic behaviour of binary packed distillation column has been simulated using back mixing models. The model solution has been obtained employing orthogonal collocation on finite element methods. The solution region is considered to be consisting of small, interconnected subregions called finite elements and optimal oprating conditions were also determined by using steady-state model based on on-line optimization method. Optimal adaptive GMV control and PID methods were applied to the system. In all the control works, the heat input to the reboiler was taken as a manipulating variable. Generally, theoretical and experimental results were in agreement with each other and it was observed that GMV methods controlled the system better than PID control method.

Steady-state optimization for biodiesel production in a reactive distillation column

Biodiesel is a notable alternative to the widely used petroleum-derived diesel fuel since it can be generated by domestic natural sources such as soybeans, rapeseeds, coconuts, and even recycled cooking oil, and thus reduces dependence on diminishing petroleum fuel from foreign sources. The advantages of biodiesel as diesel fuel are its portability, ready availability, renewability, higher combustion efficiency, lower sulfur and aromatic content, higher cetane number, and higher biodegradability. The main disadvantages of biodiesel as diesel fuel are its higher viscosity, lower energy content, higher cloud point and pour point, higher nitrogen oxide emission, lower engine speed and power, injector coking, engine compatibility, high price, and higher engine wear. Therefore, biodiesel production is confronted with two main tasks: cost reduction and ecological restrictions. Also, beside these objectives, the social involvements of biodiesel production and use must not be neglected. The best decisions regarding all these aspects imply solving optimization problems. Biodiesel is defined as a mixture of fatty acid alkyl esters which are commonly produced from triglycerides and alcohol through trans-esterification reaction in the presence of alkali catalysts. In this work, canola oil and methanol were used in this research as the feedstocks, and potassium hydroxide and potassium methoxide were used as different formulations of catalysts. A laboratory-scale continuous-flow reactive distillation column system was simulated at optimum conditions by Aspen HYSYS Software. The homogeneous alkali and acid catalyzed were applied to the system. The non-catalytic reaction, where the absence of catalyst simplifies the purification procedures and the products can be easily separated. The critical operating conditions and high consumption of methanol and energy make it uneconomical. Based on the optimization of energy integration and methanol recovery strategies, optimization strategies were assessed for saving energy and recovery methanol.

Reducing Global Warming by Process Integration

In an attempt to contribute to the reduction of global warming, the integration of both reaction and separation in a single unit to bring about an improvement in the energy efficiency of the overall process is studied in this chapter. The esterification reaction between acetic acid and ethanol for the production of ethyl acetate (desired product) and water (by-product) is used as the case study. In order to demonstrate the role of the integrated process in global warming reduction, both the conventional system and the integrated one are studied. The conventional system is made up of a reactor (in which the chemical reaction takes place) and a distillation column (in which the desired product is purified) whereas the integrated system consists of a single unit having a single main column divided into three sections (rectification section, reaction section, and stripping section). Comparing the two systems, the results that are obtained from the studies reveal that, for the integrated system, the heat released to the atmosphere from the condenser (that is, the condenser duty) and the one supplied to the reboiler (the reboiler duty) are less than those of the conventional system. These results have actually shown the importance of process integration in reducing global warming and increasing process efficiency. It is therefore suggested to the industrial engineers to always integrate their processes, where possible, in order to contribute their quotas in reducing global warming.