Browsing by Author "Muhammad Ali Akbar"

Vision TN50 “Transformasi Nasional 2050” of Malaysia is encouraging institutions to produce more talent for digital transformation of industries and enterprises. One of them is a new domain for Internet of Things (IoT) technologies where the students are required to develop their skills and knowledge in the field of advance control like industrial automation & robotics. Engineering platforms are the key component of engineering labs for teaching the programing and hardware concepts. The existing engineering labs having industrial standard PLC platform, but these platforms are lacking in term of advance control and IoT integration features. Few institutions are offering IoT and its being taught on the prototype scale controllers and microcomputers, which does not fulfil the design of industrial grade applications. In this research, a multipurpose educational and research platform (MERP) for learning advance control and IoT technologies was designed and developed. The MERP is purposely developed for technical institutions to teach and train the undergraduates and postgraduate students. The platform is divided into two parts; 1) the main panel and 2) the application panels (Motor control and Project board). This low-cost developed platform is using industrial standard controller which is suitable for industrial and enterprise applications prototyping. Three modules were designed to teach / train the students on this platform; 1) introduction to Industry 4.0 & IoT, 2) controller programming, configuration and machine to machine (M2M) communication, and 3) design and development of web and mobile application. The integration of MERP is done in engineering degree program at university level purposely to validate the impact of MERP in engineering education using survey. Based on results analysis, students had learnt to use this platform very effectively by developing a real time IoT application. The developed MERP and its contents meet the IoT layers to provide insight of this technology. In conclusion, the developed modules outcome is in-line with course objectives and IoT pillars to enhance student skills in industrial control technique. It is necessary to expose the student with the latest update of control technologies using this latest control platforms to meet industrial demand for marketable student in the future.

Industries commonly use natural gas boilers to fulfil the hot water and steam requirement of the factories. The emission of greenhouse gasses increase in the environment due to the vast usage of natural gas boilers. Therefore, solar heat for industrial process (SHIP) systems are being introduced around the globe to supply hot water and steam in the processing activities of factories. In the first part of this work, the performance of the SHIP system is evaluated, which was installed at the oleochemical factory's rooftop in Johor Bahru, Malaysia. The SHIP system comprises 75 evacuated tube collector (ETC) solar thermal panels. The performance is evaluated on monthly and quarterly data monitored from January 2021 to December 2021. First, a comprehensive analysis is conducted on nine performance parameters; useful energy, delivered energy, supply temperature, collector inlet temperature, collector outlet temperature, temperature difference, collector efficiency, system efficiency, and system losses. Secondly, an hour ahead forecasting of solar thermal output is performed quarterly for the SHIP system over the same period (JAN-DEC 2021), based on forecasting accuracy measurement parameters such as RMSE, MSE, r and R2. A deep learning method (LSTM-RNN) is proposed and compared with other machine learning (ANN, CNN & NARX) for an hour ahead forecasting of SHIP system output on a quarterly basis for one-year data. Finally, particle swarm optimization (PSO) is used as a hybrid forecasting technique (PSO-LSTM) to tune the hyperparameters of the developed deep learning method to enhance its forecasting accuracy and compared with LSTM-RNN, SSA-LSTM, and GA-LSTM. Performance analysis findings show that the SHIP system performs better, with a monthly average efficiency of 56.38% and 51.92% for useful and delivered energy, respectively. Moreover, the SHIP system has avoided 104.74 tons of CO2 emissions in one year. On the other hand, forecasting results show that the proposed deep learning technique (LSTM-RNN) has presented lower (RMSE, MSE) and higher (r and R2) compared to other techniques. The developed PSO-LSTM hybrid method gave an average reduction of (30.75% and 51.10%) and (25.99% and 44.18%) in the (RMSE and MSE) for useful and delivered energy, respectively. Furthermore, the proposed PSO-LSTM method showed higher r and R2 than GA-LSTM and SSA-LSTM. In addition, the proposed deep learning and hybrid models (PSO-LSTM) are found robust and flexible in predicting power output for the SHIP system over one year.