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Real-time optimization using gradient adaptive selection and classification from infrared sensors measurement for esterification oleic acid with glycerol

Iwan Aang Soenandi (Post Graduate Program of Agro-industrial Technology, Bogor Agricultural University, Bogor, Indonesia) (Faculty of Engineering and Computer Science, Krida Wacana Christian University, Jakarta, Indonesia)
Taufik Djatna (Post Graduate Program of Agro-industrial Technology, Bogor Agricultural University, Bogor, Indonesia)
Ani Suryani (Post Graduate Program of Agro-industrial Technology, Bogor Agricultural University, Bogor, Indonesia)
Irzaman (Department of Physics, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Bogor, Indonesia)

International Journal of Intelligent Computing and Cybernetics

ISSN: 1756-378X

Article publication date: 12 June 2017

Abstract

Purpose

The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process efficiency. An accurate monitoring and controlling of the process can improve production yield and efficiency. The purpose of this paper is to propose a real-time optimization (RTO) using gradient adaptive selection and classification from infrared sensor measurement to cover various disturbances and uncertainties in the reactor.

Design/methodology/approach

The integration of the esterification process optimization using self-optimization (SO) was developed with classification process was combined with necessary condition optimum (NCO) as gradient adaptive selection, supported with laboratory scaled medium wavelength infrared (mid-IR) sensors, and measured the proposed optimization system indicator in the batch process. Business Process Modeling and Notation (BPMN 2.0) was built to describe the tasks of SO workflow in collaboration with NCO as an abstraction for the conceptual phase. Next, Stateflow modeling was deployed to simulate the three states of gradient-based adaptive control combined with support vector machine (SVM) classification and Arduino microcontroller for implementation.

Findings

This new method shows that the real-time optimization responsiveness of control increased product yield up to 13 percent, lower error measurement with percentage error 1.11 percent, reduced the process duration up to 22 minutes, with an effective range of stirrer rotation set between 300 and 400 rpm and final temperature between 200 and 210°C which was more efficient, as it consumed less energy.

Research limitations/implications

In this research the authors just have an experiment for the esterification process using glycerol, but as a development concept of RTO, it would be possible to apply for another chemical reaction or system.

Practical implications

This research introduces new development of an RTO approach to optimal control and as such marks the starting point for more research of its properties. As the methodology is generic, it can be applied to different optimization problems for a batch system in chemical industries.

Originality/value

The paper presented is original as it presents the first application of adaptive selection based on the gradient value of mid-IR sensor data, applied to the real-time determining control state by classification with the SVM algorithm for esterification process control to increase the efficiency.

Keywords

Acknowledgements

The authors would like to acknowledge the financial support of Ministry of Research Technology and Higher Education Republic of Indonesia with contract number: 095/K3/KM/2015.

Citation

Aang Soenandi, I., Djatna, T., Suryani, A. and Irzaman, I. (2017), "Real-time optimization using gradient adaptive selection and classification from infrared sensors measurement for esterification oleic acid with glycerol", International Journal of Intelligent Computing and Cybernetics, Vol. 10 No. 2, pp. 130-144. https://doi.org/10.1108/IJICC-06-2016-0022

Publisher

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Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited