8th International Conference on Intelligent Systems, Modelling and Simulation, ISMS2018

Efficient of Soft Sensor Modelling for Advanced manufacturing Systems by Applying Hybrid Intelligent Soft Computing Techniques

Authors Affiliation 1- Ali Hussein Al-Jlibawi Center for Advanced Power and Energy Research (CAPER) / UPM 2- Associate Professor Mohammad Lutfi Bin Othman 3- Dr. Muayed S. Alhuseiny Faculty Of Engineering / Wassitt University 4-Prof. Ishak Bin Aris 5- Prof. Madya Dr. Samsul Bahari Faculty of Engineering / UPM

Outlines Introduction Literature review Problem statement Objectives of the study Methodology Flow chart anticipated of the study results Industry 4.0 Conclusion

Intelligent computation Introduction Soft Sensor is an inferential model based on software techniques to estimate the value of a process variable. Intelligent computation Software Physical sensor

Introduction The useful of soft sensor its ability to infer in real time a measurement otherwise available only after significant delays by applying analyzer and lab tests. In contrast to a physical sensor that directly measures the value of the process variable.

Introduction Soft Sensors focus on the process of estimation of any system variable or product quality by using mathematical models, substituting some physical sensors and using data acquired from some other available ones. (Fortuna, et.al. 2007) There are a number of reasons why soft sensors can be profitability used in industrial application; currently they are becoming routine tools with the trend moving from open-loop information tools for the operator towards sensors in closed –loop inferential and/or adaptive control schemes.

Oil Refinery For the Successful monitoring and control of chemical plants there are an important quality variables that are difficult to measure on-line, due to limitation such as cost, reliability, and long dead time. These measurement limitation may cause important problems such as product and /or quality loss, energy loss, toxic byproduct generation, and safety problems. These are all challenges that can be successfully bridged with the application of soft sensors.

Key Challenges in Developing Soft Sensors Good quality data are not uniformly available. Erroneous samples in process variables, as well as analyzer and lab measurements occur due to poor calibration, measurement error, computer interface errors, etc. There could be a mismatch between analyzer and lab measurements, in terms of both time and value. Analyzer and lab measurements have different sampling intervals and time delays, which causes mismatch in time of measurement. Sometimes there is also a mismatch between the analyzer value and the lab value. Therefore, alignment of the data to counteract the mismatches is necessary. Change of process operating conditions in the refinery is quite common. Each feed corresponds to a different operating condition. In addition, new crudes and unit operation conditions are likely in the feature. The process is a large scale one with hundreds of variables. It is impossible to include all of them in the inferential model. Therefore, some analysis must be done to identify the important variables as inputs to the soft sensor.

Literature review ISMS2018/1570443995-3.doc The gap of previous study: During review of literatures of journals and conference papers, we found many limitations in the models that have been used in different industries. The boundaries can be concluded as: the models work with limited data (Oil refineries processes), difficulty in maintenance for some models of soft sensors or used manually maintenance, lack of data in some industries that lead to use just the models that need less number of data, complexity and nonlinearity of operating the models, and last but not the least the privacy of sharing data with industries.

Problem statement the changing of processing data real-time measurement of product quality is generally difficult Complexity , nonlinearity , and the data quality of the process data the privacy barriers Soft sensor in oil refinery , correlation between variables. a lake in literature

Objectives of the study To optimize the performance of soft sensors modelling hybrid neuro-fuzzy (soft computing methodology) based on RST and using DE as optimal discretizing method for the continues data, in Isomerization processes in refineries processes control systems, to predict and prognosis the quality of cuts (fractions) and adopt the changing in the process data. To enhance the quality of data by combining the decision tables of soft computing model from different sources (source from another party) and the database of the case studies. To Prove the validation of the suggested improving by compare using DE with GA for the same model. 1 2 3

data driven soft sensors Methodology data driven soft sensors the model required less process knowledge and also exploit historical operating data to extract the correlation between variables Experts knowledge the need for expert knowledge to know the processes variables correlation more than knowing the process itself. Data fusion is the process of integrating multiple data sensors to produce more consistent, accurate , and useful information than that provided by any individual data source.

Methodology The process data will be provided and collected from Petronas Research Center in Malaysia. And from Ministry of Oil (research center) in Iraq .

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Methodology

Research Case Study Oil Refineries in Malaysia. Oil Refineries in Iraq

Oil Refineries/ Malaysia Very light and sweet Sulfur content=0.08 percent per weight API gravity=44° LNG =1.2 TCF (trillion cubic feet) Total Production of crude oil= 752,000 bbl/d Total Refining of oil =596,000 bbl/d

Oil Refineries/ Iraq various grade Sulfur content=2.1% -3.4% API gravity=22°-48° LNG = Total Production of crude oil= 4,448,000 bbl/d Total Refining of oil =1,092,000 bbl/d

Anticipated of the study results The study contribute in improving Artificial intelligent and machine learning algorithms , improving hybrid model neuro-fuzzy model based on rough set theory and Differential Evolution. The study aim is the soft sensor applications development in oil refineries field to predict the quality of products and enhance the yield of Gasoline in isomerization processes in oil refineries. Cooperating between different industries and integrate the big data analysis, towards the trend of industry 4.0

Industry 4.0 Since The rapid transformations in industries, the advanced technologies relevant to industries such as the Internet of Thing (IoT) , advanced materials, additive manufacturing, advanced analytic, artificial intelligence, and robotic. So our research we will focus on advanced analytic, artificial intelligence in industry fields to achieve and integrated in industry 4.0 in oil refineries control systems.

Conclusion The main advantage for Fuzzy logic system in translate the knowledge experts with industry process in rule sets, make it close to human cognitive in solving and managing the complex problems. In comparable the Neural Network benefit in parallel computing and self-learning and training, make these two methods combine suitable for supporting advanced process control systems in oil refineries and especially for Isomerization processes and predict the quality parameters. So, what is the new will be proposed and performed in this work? The proposed model will deal with the way of introducing data sets for different case studies for the same industrial processes. Then use Deferential Evolution to optimize the data sets of the fuzzy logic system. That follow by RST that will be used to reduce the rules set for the fuzzy logic. That mean our suggestion will first increase the size of data sets for discovering and unknown data, then reduce and delete the extra or redundant data. Adding on the above mentioned is the using of rough set theory and differential evolution algorithms for reducing the rules of fuzzy set and optimizing the input of the soft sensor model based on fuzzy logic and neural network, is new and novel techniques in soft sensor applications.