Andrey Romanenko

Modeling the effect of mixing in biodiesel production

The transesterification reaction models available in the literature are valid only for one particular mixing condition. In this work, a modeling strategy is presented in order to predict the effect of mixing conditions in the transesterification process. The proposed methodology was applied to independent sets of experimental data available in the literature that show the dependency of the transesterification reaction on the frequency of rotation of the stirrer. The accuracy of the developed models corroborates the validity of the proposed modeling approach.

A Nonlinear Model Predictive Control Framework as Free Software: Outlook and Progress Report

Model predictive control (MPC) has been a field with considerable research efforts and significant improvements in the algorithms. This has led to a fairly large number of successful industrial applications. However, many small and medium enterprises have not embraced MPC, even though their processes may potentially benefit from this control technology. We tackle one aspect of this issue with the development of a nonlinear model predictive control package NEWCON that will be released as free software. The work details the conceptual design, the control problem formulation and the implementation aspects of the code. A possible application is illustrated with an example of the level and reactor temperature control of a simulated CSTR. Finally, the article outlines future development directions of the NEWCON package.

Stiction Detection and Quantification as an Application of Optimization

Stiction is a major problematic phenomenon affecting industrial control valves. An approach for detection and quantification of valve stiction using an one-stage optimization technique is proposed. A Hammerstein Model that comprises a complete stiction model and a process model is identified from industrial process data. Some difficulties in the identification approach are pointed out and strategies to overcome them are suggested, namely the smoothing of discontinuity points. A simulation study demonstrates the application of the proposed technique.

Parameter Estimation of the Kinetic \(\alpha \)-Pinene Isomerization Model Using the MCSFilter Algorithm

This paper aims to illustrate the application of a derivative-free multistart algorithm with coordinate search filter, designated as the MCSFilter algorithm. The problem used in this study is the parameter estimation problem of the kinetic \(\alpha \)-pinene isomerization model. This is a well known nonlinear optimization problem (NLP) that has been investigated as a case study for performance testing of most derivative based methods proposed in the literature. Since the MCSFilter algorithm features a stochastic component, it was run ten times to solve the NLP problem. The optimization problem was successfully solved in all the runs and the optimal solution demonstrates that the MCSFilter provides a good quality solution.

Modeling and nonlinear MPC of a dividing-wall column for separation of Benzene-Toluene-p-Xylene: a simulation case study

Abstract This work focuses on the modeling and Nonlinear Model Predictive Control (NMPC) of a simulated atmospheric Dividing-Wall Column (DWC) for the separation of a Benzene-Toluene-p-Xylene (BTX) mixture. This process is highly nonlinear and even features gain inversions. The development of the dynamic first-principle model of the DWC is made using the Daedalus Modeling Framework. The simulation results show the potential of the application of NMPC technology to control DWC units.

Parameter estimation of a pulp digester model with derivative-free optimization strategies

The work concerns the parameter estimation in the context of the mechanistic modelling of a pulp digester. The problem is cast as a box bounded nonlinear global optimization problem in order to minimize the mismatch between the model outputs with the experimental data observed at a real pulp and paper plant. MCSFilter and Simulated Annealing global optimization methods were used to solve the optimization problem. While the former took longer to converge to the global minimum, the latter terminated faster at a significantly higher value of the objective function and, thus, failed to find the global solution.The work concerns the parameter estimation in the context of the mechanistic modelling of a pulp digester. The problem is cast as a box bounded nonlinear global optimization problem in order to minimize the mismatch between the model outputs with the experimental data observed at a real pulp and paper plant. MCSFilter and Simulated Annealing global optimization methods were used to solve the optimization problem. While the former took longer to converge to the global minimum, the latter terminated faster at a significantly higher value of the objective function and, thus, failed to find the global solution.

Simulation and Advanced Control of the Continuous Biodiesel Production Process

The biodiesel industry is characterized by high fluctuations of the prices and a multiplicity of biological raw material sources. On the other hand, there exist strict quality standards imposed on the final product. Because of these factors, it is important for biodiesel plants to run their processes in the most efficient manner in order to stay competitive. One of the ways to achieve this is the use of model based approaches for design, operation, and control. In this work, that focuses on the latter two areas, a first-principle dynamic model of the main units of a biodiesel plant is developed and applied in two situations: for open-loop simulation as well as for process optimization. The former demonstrates the response observed in the process variables when the plant is subjected to a series of disturbances in the input variables. The later is built in the context of nonlinear model predictive control that determines the optimal profiles of the manipulated variables taking into account process and quality constraints as well as the associated reactant and energy costs.

Development of a Numerically Efficient Biodiesel Decanter Simulator

This chapter deals with the modelling, simulation, and control of a separator unit used in the biodiesel industry. While mechanistic modelling provides an accurate way to describe the system dynamics, it is an iterative and computationally burdensome process that arises from the need to determine the liquid-liquid equilibria via the flash calculation. These disadvantages would preclude the use of mechanistic models for process optimization or model based control. In order to overcome this problem, an alternative strategy is here suggested. It consists of maintaining the mechanistic model structure and to approximate the iterative calculations with an artificial neural network. The general approach for dataset consideration and neural network training and validation are presented. The quality of the resulting neural network is demonstrated to be high while the computation burden is significantly reduced. Finally, the obtained grey-box model is used in order to carry out dynamic simulation and control tests of the unit.

System identification as an application of optimization

The work concerns the system identification of industrial processes via the Sequential Quadratic Programming algorithm. The proposed approach, testing scenarios, and the system identification results are discussed. The tool is tested with two datasets, the first one collected in loco from an industrial process and the second one generated with a plant simulator of a continuous stirred tank reactor, a system widely used in industry. In both cases, the resulting models capture well the process dynamics.

A system for chemical process control and supervision based on real-time Linux

Publisher Summary This chapter analyzes a system for chemical process control and supervision based on real-time Linux. It describes a general system for hierarchical supervision and control of complex chemical plants, using the real-time extensions of the Linux operating system. Distributed control systems (DCS) are widely used throughout the chemical process industries and generally regarded as providing an efficient approach for the control of complex systems. Their ability to successively decompose control problems into more manageable tasks eliminates performance bottlenecks, and provides a reliable framework for process control. Several factors contributed to the success of this application in chemical plants, such as low cost and abundant availability of distributed computing and network equipment, acceptance of key industry standards by major manufacturers, and the growing capability to integrate the control with business and manufacturing layers. The reliability of this approach is particularly suited for use with complex and heterogeneous DCS, where stringent coordination and timing requirements can be crucial. The system has an open modular nature, which can be easily extended and integrated with additional open-source tools. This nature makes it also particularly suitable as a research and educational tool.