• Energy Research

The transportation sector has been reported as a key contributor to the emissions of greenhouse gases responsible for global warming. Hence, the need for the introduction of electric vehicles (EVs) into the transportation sector. However, the competitiveness of the EVs with the conventional internal combustion engine vehicles has been a bone of contention. Life cycle cost analysis (LCCA) is an important tool that can be employed to determine the competitiveness of a product in its early stage of production. This review examines different published articles on LCCA of EVs using Scopus and Web of Science databases. The time trend of the published articles from 2001 to 2019 was examined. Moreover, the LCC obtained from the different models of EVs were compared. There was a growing interest in research on the LCC of EVs as indicated by the upward increase in the number of published articles. A variation in the LCC of the different EVs studied was observed to depend on several factors. Based on the LCC, EVs were found not yet competitive with conventional internal combustion engine cars due to the high cost of batteries. However, advancement in technologies with incentives could bring down the cost of EV batteries to make it competitive in the future.

This study investigates the use of a non-linear autoregressive exogenous neural network (NARX) model to investigate the nexus between energy usability, economic indicators, and carbon dioxide (CO2) emissions in four Association of South East Asian Nations (ASEAN), namely Malaysia, Thailand, Indonesia, and the Philippines. Optimized NARX model architectures of 5-29-1, 5-19-1, 5-17-1, 5-13-1 representing the input nodes, hidden neurons and the output units were obtained from the series of models configured. Based on the relationship between the input variables, CO2 emissions were predicted with a high correlation coefficient (R) > 0.9. and low mean square errors (MSE) of 3.92 × 10−21, 4.15 × 10−23, 2.02 × 10−19, 1.32 × 10−20 for Malaysia, Thailand, Indonesia, and the Philippines, respectively. Coal consumption has the highest level of influence on CO2 emissions in the four ASEAN countries based on the sensitivity analysis. These findings suggest that government policies in the four ASEAN countries should be more intensified on strategies to reduce CO2 emissions in relationship with the energy and economic indicators.

Abstract Glycerol is the main by-products obtained from the transesterification of vegetable oils and animal fats to produce biodiesel which is an important biofuel used for transportation. The increase in the global energy demand has pushed up the production of biodiesel with a corresponding increase in glycerol production over the years. The thermo-catalytic process is gaining wide popularity as sustainable technical routes of converting glycerol to renewable hydrogen. There exists a great potential of utilizing hydrogen as a critical part of a more sustainable and secure energy mix. Hence, this study focusses on the review of the recent advances and development in the thermo-catalytic conversion of glycerol to renewable hydrogen in the last one decade. The analysis of the reviewed articles showed that substantial efforts had been made in the application of thermo-catalytic process for the conversion of glycerol to renewable hydrogen. Glycerol reforming using steam, carbon dioxide (CO2) and oxygen (O2) have received significant research attention and have been found to have great potential as technological routes for hydrogen production. Whereas, the use of the photocatalytic glycerol reforming has the advantages of energy-saving by utilizing the vast available solar resources and suitable photocatalysts. However, each of the thermo-catalytic processes exhibits inherent challenges which have been a bottleneck to the development of the process to industrial scales. Nevertheless, the prospect of employing each of the thermo-catalytic processes for hydrogen production via glycerol conversion was identified with the possible suggestion of strategies of overcoming the challenges.

The need to mitigate CO2 emissions from the transportation sector has necessitated the adoption of electric vehicles (EVs) and other forms of alternative vehicles. Despite the global rise of EVs demand as a complementary means of green transportations, the level of adoption in Malaysia is still not encouraging. Therefore, this study aimed to investigate the cost competitiveness of EVs in comparison with Hybrid Electric Vehicles (HEVs) and an Internal Combustion Vehicle (ICV) based on Malaysia scenarios. Using the existing data in Malaysia, life cost analysis (LCC) of two EVs was computed and compared with HEVs and ICVs. The study shows that Nissan leaf and BMW i3s EVs with LCC of $1.75 and $2.5 per km are not cost-competitive based on prevalent data available in Malaysia compared to the HEVs and ICV. Based on the sensitivity analysis, changes in the components of the operating costs significantly influence the accumulated cost of ownership of the EVs whereas the cost of ownership of the HEVs and ICVs did not experience any significant influence. The findings from this study could serve as bases for policymakers to formulate appropriate policies and strategies to improve the competitiveness of EVs in Malaysia.

The thermochemical processes such as gasification and co-gasification of biomass and coal are promising route for producing hydrogen-rich syngas. However, the process is characterized with complex reactions that pose a tremendous challenge in terms of controlling the process variables. This challenge can be overcome using appropriate machine learning algorithm to model the nonlinear complex relationship between the predictors and the targeted response. Hence, this study aimed to employ various machine learning algorithms such as regression models, support vector machine regression (SVM), gaussian processing regression (GPR), and artificial neural networks (ANN) for modeling hydrogen-rich syngas production by gasification and co-gasification of biomass and coal. A total of 12 machine learning algorithms which comprises the regression models, SVM, GPR, and ANN were configured, trained using 124 datasets. The performances of the algorithms were evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). In all cases, the ANN algorithms offer superior performances and displayed robust predictions of the hydrogen-rich syngas from the co-gasification processes. The R2 of both the Levenberg-Marquardt- and Bayesian Regularization-trained ANN obtained from the prediction of the hydrogen-rich syngas was found to be within 0.857-0.998 with low prediction errors. The sensitivity analysis to determine the effect of the process parameters on the model output revealed that all the parameters showed a varying level of influence. In most of the processes, the gasification temperature was found to have the most significant influence on the model output.

La conversion thermo-catalytique et biochimique de la biomasse en gaz de synthèse riche en hydrogène a été largement rapportée avec moins d'accent sur les implications environnementales des processus. Cette mini-revue présente un aperçu des différentes voies thermo-catalytiques de conversion de la biomasse en gaz de synthèse riche en hydrogène ainsi que leur impact environnemental étudié à l'aide de la méthodologie d'évaluation du cycle de vie. La revue a révélé que la plupart des auteurs utilisaient des procédés de gazéification de la biomasse, de pyrolyse de la biomasse, de reformage et de fermentation pour la production de gaz de synthèse riche en hydrogène. Le potentiel de réchauffement climatique a été observé comme l'impact environnemental le plus important signalé dans les articles examinés. On a constaté que les émissions d'équivalent CO2 varient selon chacun des processus et le type de charge d'alimentation utilisé. Les tendances de la littérature montrent que les processus thermo-catalytiques et biochimiques présentent des avantages concurrentiels et un potentiel de concurrence favorable par rapport à la technologie existante utilisée pour la production d'hydrogène. Néanmoins, il n'est pas possible de déterminer si ces technologies doivent être exclues des charges environnementales. Ce mini-examen pourrait être un guide rapide pour l'intérêt futur de la recherche sur l'impact environnemental de la production de gaz de synthèse riche en hydrogène par conversion thermo-catalytique et biochimique de la biomasse. Se ha informado ampliamente sobre la conversión termocatalítica y bioquímica de biomasa en gas de síntesis rico en hidrógeno con menos énfasis en las implicaciones ambientales de los procesos. Esta mini revisión presenta una descripción general de las diferentes rutas termocatalíticas de conversión de biomasa en gas de síntesis rico en hidrógeno, así como su impacto ambiental investigado utilizando la metodología de evaluación del ciclo de vida. La revisión reveló que la mayoría de los autores emplearon procesos de gasificación de biomasa, pirólisis de biomasa, reformado y fermentación para la producción de gas de síntesis rico en hidrógeno. El potencial de calentamiento global se observó como el impacto ambiental más significativo informado en los artículos revisados. Se encontró que las emisiones equivalentes de CO2 varían con cada uno de los procesos y el tipo de materia prima utilizada. Las tendencias de la literatura muestran que tanto los procesos termocatalíticos como los bioquímicos tienen ventajas competitivas y potencial para competir favorablemente con la tecnología existente utilizada para la producción de hidrógeno. Sin embargo, no se puede determinar que estas tecnologías deban excluirse de las cargas ambientales. Esta mini revisión podría ser una guía rápida para futuros intereses de investigación sobre el impacto ambiental de la producción de gas de síntesis rico en hidrógeno mediante la conversión termocatalítica y bioquímica de biomasa. The thermo-catalytic and biochemical conversion of biomass to hydrogen-rich syngas has been widely reported with less emphasis on the environmental implications of the processes. This mini-review presents an overview of different thermo-catalytic route of converting biomass to hydrogen-rich syngas as well as their environmental impact investigated using life cycle assessment methodology. The review revealed that most of the authors employed, biomass gasification, biomass pyrolysis, reforming and fermentative processes for the hydrogen-rich syngas production. Global warming potential was observed as the most significant environmental impact reported in the reviewed articles. The CO2 equivalent emissions were found to varies with each of the processes and the type of feedstock used. Trends from literature show that both thermo-catalytic and biochemical processes have competitive advantages and potential to compete favorable with the existing technology used for hydrogen production. Nevertheless, it cannot be ascertained that these technologies should be excluded from environmental burdens. This mini-review could be a quick guide to future research interest in environmental impact of hydrogen-rich syngas production by thermo-catalytic and biochemical conversion of biomass. تم الإبلاغ على نطاق واسع عن التحويل الحفزي الحراري والكيميائي الحيوي للكتلة الحيوية إلى غاز تخليق غني بالهيدروجين مع تركيز أقل على الآثار البيئية للعمليات. تقدم هذه المراجعة المصغرة نظرة عامة على المسار الحفاز الحراري المختلف لتحويل الكتلة الحيوية إلى غاز تخليق غني بالهيدروجين بالإضافة إلى تأثيرها البيئي الذي تم التحقيق فيه باستخدام منهجية تقييم دورة الحياة. كشفت المراجعة أن معظم المؤلفين استخدموا، تغويز الكتلة الحيوية، الانحلال الحراري للكتلة الحيوية، عمليات الإصلاح والتخمير لإنتاج غاز التخليق الغني بالهيدروجين. لوحظ أن إمكانات الاحترار العالمي هي أهم تأثير بيئي تم الإبلاغ عنه في المقالات التي تمت مراجعتها. وتبين أن انبعاثات مكافئ ثاني أكسيد الكربون تختلف باختلاف كل عملية ونوع المادة الوسيطة المستخدمة. تظهر الاتجاهات من الأدبيات أن كل من العمليات الحفازة الحرارية والكيميائية الحيوية لها مزايا تنافسية وقدرة على التنافس بشكل مواتٍ مع التكنولوجيا الحالية المستخدمة لإنتاج الهيدروجين. ومع ذلك، لا يمكن التأكد من أنه ينبغي استبعاد هذه التقنيات من الأعباء البيئية. يمكن أن تكون هذه المراجعة المصغرة دليلاً سريعًا للاهتمام البحثي المستقبلي بالتأثير البيئي لإنتاج غاز التخليق الغني بالهيدروجين عن طريق التحويل الحفاز الحراري والكيميائي الحيوي للكتلة الحيوية.

Energy modeling and forecasting are key to providing insightful energy-related policy decisions that could propel positive economic growth and sustainable development. In this study, the Non-Linear Autoregressive Exogenous Neural Networks (NARX) and Multiple Non-Linear Regression (MNLR) models were employed for modeling the final energy demand per capita in Malaysia. The non-linear relationship between the time-series data such as GDP per capita, population, crude oil supply, natural gas supply, coal and coke supply, hydropower, electricity demand per capita, and the final energy demand per capita were modeled using five NARX and MNLR models refer to as NARX-1, NARX-3, NARX-5, MNLR-1, and MNLR-2. The effect of varying the network time delay and the hidden neurons on the NARX model performance were investigated. The best model configuration was obtained using 1 network delay and 17 hidden neurons. Both the NARX-1 and MNLR-1 models accurately learn the time series data for the prediction of the final energy consumption per capita. With R2 of 0.999 and 0.996 obtained for the NARX-1 and MLNR-1, respectively, the predicted final energy demand per capita was in close agreement with the actual values. Using the NARX-1 and MNLR-1 models, the final energy demand per capita was projected to be 2.3 toe/person and 2.1toe/person, respectively by 2050. The level of importance analysis of the NARX-1 models and the parameter estimates of the MNLR-1 model revealed that an increase in population and natural gas supply had a significant influence on the predicted final energy demand per capita.