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Far Eastern Magazine
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07.2020 Cover Story

OPTC initiates the future of AI, controlling quality and energy consumption intelligently

Far Eastern Magazine / Editorial Room

        In 2016, the era of vigorous development of artificial intelligence started after AlphaGO defeated the world's Go champion, so all enterprises actively invested in relevant development and applications. In order to be line with the pace of digital transformation of the group, Guanyin No.2 plant of Oriental Petrochemical (Taiwan) implemented the construction of industry 4.0 system during the establishment of the plant. Taking the intelligent system platform as the core, it extended VR educational training, Smart electronic inspection, intelligent logistics management, safety and energy electric board, video wall monitoring system of the in-site process, etc., to build a new intelligent chemical plant, and further introduce AI into the process application, letting quality prediction and energy management become more and more intelligent!

        

        Quality prediction and insight into opportunities

        

        As AI technology is becoming more and more mature, Oriental Petrochemical (Taiwan) technology department began to introduce AI into the process in 2018, hoping to assist in quality prediction of oxidation process and active energy management of the plant. Because in the early manufacturing process, the quality is adjusted according to the results of laboratory analysis. Sampling and analysis are separated for a period of time. When abnormal occurs, it is impossible to master the quality change immediately. Although the model predictive control (MPC) can improve this problem recently, it is not easy to build and update the traditional model, and it is not accurate in the nonlinear section. If the AI big data can be used to model, the accuracy of quality prediction can be improved.

        

        On the other hand, most of the energy management methods in the past were adjusted after the event analysis (weekly / monthly review), which resulted in low efficiency. Moreover, the alarm function of DCS (Distribution Control System) could not be used to know whether the energy consumption was abnormal under different refining capacity. Therefore, Oriental Petrochemical (Taiwan) Co., Ltd hoped that with the help of AI, the change of unit energy consumption can be monitored in real time, so as to make early response and adjustment, keep energy consumption normal and further strengthen energy conservation and emission reduction.

        

        In order to quickly get familiar with machine learning and artificial intelligence, the project team conducted a preliminary AI modeling evaluation, explored the application of AI with Rapidminer, and conducted AI deep learning modeling and verification. At the beginning, it used the off-line operation mode to collect and process the operation conditions and quality data, and then used DNN for modeling and testing. For the staff, since AI is a new scientific and technological field, there are many difficulties in the process, such as the adjustment of time difference, the great quality difference between the two production lines, the inconsistency of system data and unreasonable instrument data. Fortunately, after many tests and adjustments, a feasible model of oxidation process quality prediction was obtained and the feasibility of the project was confirmed. For prudence, Oriental Petrochemical (Taiwan) invited three manufacturers to conduct POC test (proof of concept), and finally selected one to cooperate.

        

        In this project, Oriental Petrochemical (Taiwan) provided domain knowledge related to PTA production and assisted in proper data processing, including time gap, outlier data exclusion, etc. Combined with the professional knowledge of PTA and the programming of AI algorithm, a good model was established, and excellent results were obtained in the quality prediction of oxidation process. In addition, compared with the laboratory analysis, the data of AI oxidation quality prediction can be obtained 2-3 hours earlier, so that the staff can adjust the process parameters in advance to stabilize the product quality.

        Energy management and initiative

        

        The second application to develop active energy management by using AI technology is an innovative idea of the industry. Oriental Petrochemical (Taiwan) has approached the Industrial Technology Research Institute for cooperation, which will be carried out in two stages. In the first stage, a large amount of operation data related to energy will be collected and processed properly. Then, the AI energy baseline will be established by Rapidminer. After the accuracy of the model is confirmed, the data is transferred to Industrial Technology Research Institute for coding AI algorithm and establishing AI energy baseline (EBL). In the online application, the reasonable unit energy consumption predicted by AI energy baseline model is taken as the standard, and the reasonable range is set as the control boundary, so as to present the trend of energy consumption. When the real-time energy consumption data is beyond the control range, it means that abnormal energy consumption may occur.

        It is not enough to find out abnormal immediately. Only by finding the cause of the abnormality quickly can we make the management better. Therefore, we have entered the second stage: developing the function of "automatic search for abnormal energy consumption equipment". Personnel from Oriental Petrochemical (Taiwan) and Industrial Technology Research Institute discussed about each power consumption and power generation equipment, boiler, and heat exchanger, collected and screened data, and then established the reasonable range of feature engineering of each equipment with AI technology and statistical analysis, According to the feature engineering, the data of each equipment under the capacity can be compared in real time. When it is beyond the normal range, the equipment can be regarded as abnormal equipment and the production personnel will be informed to deal with it.

        

        As a matter of fact, the whole process of AI system construction involves complex cross domain technologies, which must be clarified, communicated and discussed in a multi-faceted and in-depth manner. The project team was not tired of making the system more perfect, which not only made Oriental Petrochemical (Taiwan) achieve good results in the field of AI, but also kept the original AI code and relevant technical information, which means that it has independent AI technical ability. In the future, it can be extended to high-level process control, optimize operating conditions, and equipment fault warning system, so as to continuously improve production efficiency and reduce operating costs. The first step took by Oriental Petrochemical (Taiwan) into AI is expected to take a big step to enhance the company's digital competitiveness.

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