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MY Publications
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Solar dryer is typically used for agricultural purposes in Indonesia. There are many economically important crops requiring storage or drying under particular environmental conditions such as temperature and humidity. High temperatures inside solar dryer prevents the growth of microorganism, and quickly reduce moisture content from the substance. A hybrid solar dryer is generally considered to provide the most optimum solution, however solar panels may be expensive and they still only provide heat or energy in the daytime. Hence, we propose here a new kind of hybrid solar dryer for 24/7 optimum conditions for crops - enabled by recent advances in energy technologies as well as Industry 4.0. This study aims to create an efficient, affordable and a self-sufficient intelligent energy system that will be applied to agriculture for storage or drying purposes by measuring the energy needs for the optimal drying system. Therefore, it is crucial to estimate and assess the critical energy needs for such new systems in order to optimize and design such smart solar dryer (SSD) system especially for Indonesia's agricultural needs. We use design experience of our industry partner (PT Impack Pratama Industri, Indonesia) who has been working extensively on such solar dryer dome (SDD) based on polycarbonate material (only solar irradiation, no other technologies) and theoretical framework based on first principles in thermodynamics to estimate and assess critical energy needs for such dome with all the smart technologies. The calculation was performed based on Mollier diagram and the result still a rough estimation of energy required.
Technological developments enable low-carbon transitions to be accelerated by conceptualization systems and innovations for research and development to generate clean energy. Batteries are becoming one of the essential parts of the science of electrical power sources. Lithium-ion batteries are part of the change and development factors in technologies that significantly impact the portable devices sector and the development of electric vehicles. Designing the material structure and composition of battery manufacturing with the help of engineering system design will form a much more optimal battery. Machine learning algorithms can easily optimize the battery’s composition through battery experiment test data history to produce a more optimal battery configuration. This study is prepared to identify research gaps in topics related to machine learning for battery optimization. Related studies about machine learning for battery optimization are identified using bibliometric analysis and systematic literature review of the study search index through database Scopus-indexed publications. The results from this paper reveal energy management systems and strategies, hybrid vehicles, other optimization algorithms, battery electrodes, and the safety of batteries as the particular research gap according to machine learning for battery optimization. This paper expects research on battery optimization using machine learning methods will continue to be developed to maximize the potential of machine learning algorithms in helping the research process.
Recently, the popularity of li-ion batteries has attracted many researchers to carry out the battery’s maximum potential. Predicting batteries condition and behavior is part of the process that is considered challenging. ML algorithm is widely applied to overcome this challenge as it demonstrates a successful outcome in optimizing the complexity, accuracy, reliability, and efficiency of battery prediction. Yet, we believe there is a particular research area of battery prediction that can further be explored and enhanced with machine learning capability. Therefore, we perform a systematic literature review and bibliometric study to uncover the gap in the machine learning application in the battery prediction field. This study is divided into four stages: (1) literature search from the Scopus Database, (2) filtering the results based on keywords and prepared criteria using PRISMA method, (3) systematic review from filtered papers to provide further understanding, and (4) bibliometric analysis from visualization created in VOSViewer software. The analysis findings determine battery safety and performance prediction as a potential gap in the scope of machine learning for battery prediction research and provide some insightful information to assist future researchers. We envision this study to encourage further battery research, which will assist in the creation of better, cleaner, safer, and long-lasting energy resources.
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