Novel sustainable evaluation approach for multi-biomass supply chain

After the oil crisis held in 1973 and 1979, academicians and industry players have noticed the importance and necessity of having alternative and sustainable energy sources in future. Biological wastes, also named as “Biomass” has been cited as one of the significant sustainable energy sources. Biomass poses an ideal and substantial potential to achieve a sustainable system. However, the development of biomass industry is still relatively sluggish due to the lack of confidence of the investor to venture in this relatively new green business. This is most probably attributed to the low-maturation of biomass technologies compared to other conventional technologies, high logistics cost required for biomass transportation and uncertain market penetration barrier for the biomass-derived products. This raises the importance of having a proper biomass management system and a systematic evaluation approach to assess the sustainability performances of the biomass industry. Therefore, the ultimate goal of this thesis is to develop a sustainable multi-biomass supply chain with the aims of optimising all three sustainability dimensions simultaneously. A sustainable multi-biomass supply chain is referred as the integrated value chain of the green products, which derived from various types of biomass, starting from harvesting stage to the final products delivery stage. This thesis discusses in detail on the relevant previous research works toward the introduction of novel evaluation approach to attain different sustainable objectives (i.e., economic, environmental and social) simultaneously. The evaluation approach encompasses various components, including (i) model reduction by using P-graph integrated two-stage optimisation approach; (ii) consideration of vehicle capacity constraint for detailed transportation cost estimation; (iii) integration of various sustainability indexes using various optimisation techniques. On top of that, two novel debottlenecking approaches, one through principal component analysis (PCA) method; while another through P-graph framework, which able to identify and remove barriers that limit the sustainability performance of the biomass supply chain, are proposed. Aside from this, this thesis also aims to reduce the gaps between the researchers and industry players by developing some user-friendly and non-programming-background dependent decision-making tools. Thus, decision-makers are able to understand the insight of their problems easily without requirement of strong mathematical background. A case study in Johor, a southern state in Malaysia, which is endowed with extensive biomass resources, is used to demonstrate the effective of the proposed approaches

Novel sustainable evaluation approach for multi-biomass supply chain

After the oil crisis held in 1973 and 1979, academicians and industry players have noticed the importance and necessity of having alternative and sustainable energy sources in future. Biological wastes, also named as “Biomass” has been cited as one of the significant sustainable energy sources. Biomass poses an ideal and substantial potential to achieve a sustainable system. However, the development of biomass industry is still relatively sluggish due to the lack of confidence of the investor to venture in this relatively new green business. This is most probably attributed to the low-maturation of biomass technologies compared to other conventional technologies, high logistics cost required for biomass transportation and uncertain market penetration barrier for the biomass-derived products. This raises the importance of having a proper biomass management system and a systematic evaluation approach to assess the sustainability performances of the biomass industry. Therefore, the ultimate goal of this thesis is to develop a sustainable multi-biomass supply chain with the aims of optimising all three sustainability dimensions simultaneously. A sustainable multi-biomass supply chain is referred as the integrated value chain of the green products, which derived from various types of biomass, starting from harvesting stage to the final products delivery stage. This thesis discusses in detail on the relevant previous research works toward the introduction of novel evaluation approach to attain different sustainable objectives (i.e., economic, environmental and social) simultaneously. The evaluation approach encompasses various components, including (i) model reduction by using P-graph integrated two-stage optimisation approach; (ii) consideration of vehicle capacity constraint for detailed transportation cost estimation; (iii) integration of various sustainability indexes using various optimisation techniques. On top of that, two novel debottlenecking approaches, one through principal component analysis (PCA) method; while another through P-graph framework, which able to identify and remove barriers that limit the sustainability performance of the biomass supply chain, are proposed. Aside from this, this thesis also aims to reduce the gaps between the researchers and industry players by developing some user-friendly and non-programming-background dependent decision-making tools. Thus, decision-makers are able to understand the insight of their problems easily without requirement of strong mathematical background. A case study in Johor, a southern state in Malaysia, which is endowed with extensive biomass resources, is used to demonstrate the effective of the proposed approaches

Transition Metal Dichalcogenides for the Application of Pollution Reduction: A Review

The material characteristics and properties of transition metal dichalcogenide (TMDCs) have gained research interest in various fields, such as electronics, catalytic, and energy storage. In particular, many researchers have been focusing on the applications of TMDCs in dealing with environmental pollution. TMDCs provide a unique opportunity to develop higher-value applications related to environmental matters. This work highlights the applications of TMDCs contributing to pollution reduction in (i) gas sensing technology, (ii) gas adsorption and removal, (iii) wastewater treatment, (iv) fuel cleaning, and (v) carbon dioxide valorization and conversion. Overall, the applications of TMDCs have successfully demonstrated the advantages of contributing to environmental conversation due to their special properties. The challenges and bottlenecks of implementing TMDCs in the actual industry are also highlighted. More efforts need to be devoted to overcoming the hurdles to maximize the potential of TMDCs implementation in the industry

Catalytic thermal degradation of Chlorella Vulgaris: Evolving deep neural networks for optimization

The aim of this study is to identify the optimum thermal conversion of Chlorella vulgaris with neuro-evolutionary approach. A Progressive Depth Swarm-Evolution (PDSE) neuro-evolutionary approach is proposed to model the Thermogravimetric analysis (TGA) data of catalytic thermal degradation of Chlorella vulgaris. Results showed that the proposed method can generate predictions which are more accurate compared to other conventional approaches (>90% lower in Root Mean Square Error (RMSE) and Mean Bias Error (MBE)). In addition, Simulated Annealing is proposed to determine the optimal operating conditions for microalgae conversion from multiple trained ANN. The predicted optimum conditions were reaction temperature of 900.0 °C, heating rate of 5.0 °C/min with the presence of HZSM-5 zeolite catalyst to obtain 88.3% of Chlorella vulgaris conversion

Catalytic thermal degradation of Chlorella Vulgaris: Evolving deep neural networks for optimization

The aim of this study is to identify the optimum thermal conversion of Chlorella vulgaris with neuro-evolutionary approach. A Progressive Depth Swarm-Evolution (PDSE) neuro-evolutionary approach is proposed to model the Thermogravimetric analysis (TGA) data of catalytic thermal degradation of Chlorella vulgaris. Results showed that the proposed method can generate predictions which are more accurate compared to other conventional approaches (>90% lower in Root Mean Square Error (RMSE) and Mean Bias Error (MBE)). In addition, Simulated Annealing is proposed to determine the optimal operating conditions for microalgae conversion from multiple trained ANN. The predicted optimum conditions were reaction temperature of 900.0 °C, heating rate of 5.0 °C/min with the presence of HZSM-5 zeolite catalyst to obtain 88.3% of Chlorella vulgaris conversion

Elucidation of single atom catalysts for energy and sustainable chemical production: Synthesis, characterization and frontier science

The emergence of single atom sites as a frontier research area in catalysis has sparked extensive academic and industrial interest, especially for energy, environmental and chemicals production processes. Single atom catalysts (SACs) have shown remarkable performance in a variety of catalytic reactions, demonstrating high selectivity to the products of interest, long lifespan, high stability and more importantly high atomic metal utilization efficiency. In this review, we unveil in depth insights on development and achievements of SACs, including (a) Chronological progress on SACs development, (b) Recent advances in SACs synthesis, (c) Spatial and temporal SACs characterization techniques, (d) Application of SACs in different energy and chemical production, (e) Environmental and economic aspects of SACs, and (f) Current challenges, promising ideas and future prospects for SACs. On a whole, this review serves to enlighten scientists and engineers in developing fundamental catalytic understanding that can be applied into the future, both for academia or valorizing chemical processes

Prioritization of sustainability indicators for promoting the circular economy: The case of developing countries

The concept of the circular economy has gained well-recognition across the world for the past decades. With the heightening risk of the impact of climate change, resource scarcity to meet the increasing world population, the need to transition to a more sustainable development model is urgent. The circular economy is often cited as one of the best solutions to support sustainable development. However, the diffusion of this concept in the industrial arena is still relatively slow, particularly in the developing country, which collectively exerts high potential to be the world’s largest economies and workforce. It is crucial to make sure that the development of these nations is sustainable and not bearing on the cost of future generation. Thus, this work aims to provide a comprehensive review of the circular economy concept in developing country context. Furthermore, a novel model is proposed by adopting Fuzzy Analytics Network Process (FANP) to quantify the priority weights of the sustainability indicators to provide guidelines for the industry stakeholders at different stages of industry cycle to transition toward the circular economy. The results revealed that improvement in economic performance and public acceptance are they key triggers to encourage stakeholders for sustainable development. The outcomes serve as a reference to enhance the overall decision-making process of industry stakeholders. Local authorities can adopt the recommendations to design policy and incentive that encourage the adoption of circular economy in real industry operation to spur up economic development, without neglecting environmental well-being and jeopardizing social benefits

Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries

Integrated refineries and industrial processing plant in the real-world always face management and design difficulties to keep the processing operation lean and green. These challenges highlight the essentiality to improving product quality and yield without compromising environmental aspects. For various process system engineering application, traditional optimisation methodologies (i.e., pure mix-integer non-linear programming) can yield very precise global optimum solutions. However, for plant-wide optimisation, the generated solutions by such methods highly rely on the accuracy of the constructed model and often require an enumerate amount of process changes to be implemented in the real world. This paper solves this issue by using a special formulation of correlation-based principal component analysis (PCA) and Design of Experiment (DoE) methodologies to serve as statistical process optimisation for industrial refineries. The contribution of this work is that it provides an efficient framework for plant-wide optimisation based on plant operational data while not compromising on environmental impacts. Fundamentally, PCA is used to prioritise statistically significant process variables based on their respective contribution scores. The variables with high contribution score are then optimised by the experiment-based optimisation methodology. By doing so, the number of experiments run for process optimisation and process changes can be reduced by efficient prioritisation. Process cycle assessment ensures that no negative environmental impact is caused by the optimisation result. As a proof of concept, this framework is implemented in a real oil re-refining plant. The overall product yield was improved by 55.25% while overall product quality improved by 20.6%. Global Warming Potential (GWP) and Acidification Potential (AP) improved by 90.89% and 3.42% respectively

Adaptive Analytical Approach to Lean and Green Operations

Recent problems faced by industrial players commonly relates to global warming and depletion of resources. This situation highlights the importance of improvement solutions for industrial operations and environmental performances. Based on interviews and literature studies, manpower, machine, material, money and environment are known as the foundation resources to fulfil the facility's operation. The most critical and common challenge that is being faced by the industrialists is to perform continuous improvement effectively. The needs to develop a systematic framework to assist and guide the industrialist to achieve lean and green is growing rapidly. In this paper, a novel development of an adaptive analytic model for lean and green operation and processing is presented. The development of lean and green index will act as a benchmarking tool for the industrialist. This work uses the analytic hierarchy process to obtain experts opinion in determining the priority of the lean and green components and indicators. The application of backpropagation optimisation method will further enhance the lean and green model in guiding the industrialist for continuous improvement. An actual industry case study (combine heat and power plant) will be presented with the proposed lean and green model. The model is expected to enhance processing plant performance in a systematic lean and green manner

Adaptive Analytical Approach to Lean and Green Operations

Recent problems faced by industrial players commonly relates to global warming and depletion of resources. This situation highlights the importance of improvement solutions for industrial operations and environmental performances. Based on interviews and literature studies, manpower, machine, material, money and environment are known as the foundation resources to fulfil the facility's operation. The most critical and common challenge that is being faced by the industrialists is to perform continuous improvement effectively. The needs to develop a systematic framework to assist and guide the industrialist to achieve lean and green is growing rapidly. In this paper, a novel development of an adaptive analytic model for lean and green operation and processing is presented. The development of lean and green index will act as a benchmarking tool for the industrialist. This work uses the analytic hierarchy process to obtain experts opinion in determining the priority of the lean and green components and indicators. The application of backpropagation optimisation method will further enhance the lean and green model in guiding the industrialist for continuous improvement. An actual industry case study (combine heat and power plant) will be presented with the proposed lean and green model. The model is expected to enhance processing plant performance in a systematic lean and green manner