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Fuzzy Logic Model for Deodorizer Troubleshooting in Palm Oil Refining
Intan Suhairi Salleh,Khairiyah Mohd-Yusof,Gholamreza Zahedi 제어로봇시스템학회 2012 제어로봇시스템학회 국제학술대회 논문집 Vol.2012 No.10
Deodorizer failure roots from many sources that disrupting plant operation. A correct action for remedies during troubleshooting can save company time and profit. In this study based on real plant data, a fuzzy logic model was developed to troubleshoot the root cause of problem for deodorizer failure. An abnormality of deodorizer root cause of problem was monitored through normal condition baseline. In order to build the fuzzy troubleshooting system, the linguistic data was gathered by interviewing palm oil refining plant workers that included experienced plant operators, supervisors and executives. Numerical data were collected by reviewing Standard Operating Procedure (SOP), Process and Instrumentation Diagram (P&ID), plant manual, etc. Root cause of deodorizer failure was separated into seven categories: Fatty Acid Distillate (FAD), cooling tower, hotwell, sparging steam, High Pressure Boiler (HPB), other vacuum parameter and others. The proposed model was successfully able to predict the action needed for deodorizer failure. The model can be employed for training to inexperience workers and operators.
Fuzzy Logic Model for Degumming and Bleaching Troubleshooting in Palm Oil Refining
Nur Syuhada’ Mohamad Ali,Khairiyah Mohd Yusof 제어로봇시스템학회 2016 제어로봇시스템학회 국제학술대회 논문집 Vol.2016 No.10
Failures at degumming and bleaching process in palm oil refining affect plant performance, production delay, and loss to the company. In current practice, troubleshooting in these process relies on human knowledge and trial and error method which can caused other failures and time consuming. In this study, fuzzy logic model was developed for troubleshooting degumming and bleaching process using Mamdani approached. Operating conditions at bleacher vessel, ejector, condenser, hotwell and cooling tower system were selected as input and output variables in the fuzzy logic model development. Qualitative and numerical data were collected in this study by interviewing plant workers, observing distributed control system (DCS), reviewing standard operating procedure (SOP), operation checklist and plant manuals. The relationship between input and output variables were described by fuzzy membership function and fuzzy rules. Centre of gravity method (COG) method was used for defuzzification. The proposed model was tested with real plant data and the model was shown successfully perform troubleshooting task and suggest necessary action. Therefore, this model has the potential to employed by operator and inexperience workers in palm oil industry.
Artificial Neural Network-Based Model for Quality Estimation of Refined Palm Oil
Nurul Sulaiha Sulaiman,Khairiyah Mohd Yusof 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
The goal of this study is to develop an accurate artificial neural network (ANN)-based model to predict significant quality of refined palm oil which is Free Fatty Acid (FFA) content. The variables; FFA content, Iodine Value (IV), moisture content, bleaching earth and citric acid dosage as well as the pressure and temperature of the deodorizer is used to build the ANN prediction model. A feed forward neural network was designed using a back-propagation training algorithm. Comparison of ANN predicted result with industrial data was made. It is proven in this study that ANN can be used to estimate the quality of refined palm oil. Therefore, the model can be further implemented in palm oil refinery plant as the prediction system of the refined oil quality.