An adaptive analytical approach for lean and green framework

The recent global supply chain has exposed the industrialists in a tough position when dealing with uncertain global issues such as political changes, fluctuation on foreign exchange, raw material supplies and recession. As these challenges are inevitable, the industrialists constantly seek for alternative solutions to stay competitive in the market. The recent advancement in Industry Revolution 4.0 (IR4.0) can bring many advantages and benefits to the industrialist. As new technology required high investment cost and development of new talent, many industrialists are not confident in investing in the technology especially during the bad economy time. Therefore, the development of adaptive lean and green (L&G) framework is to build the industrialist’s confident with the elements of IR4.0 to enhance operational and environmental performance while transitioning the industry towards IR4.0. Based on the real-time condition, the L&G model incorporates both quantitative and qualitative data input for both process improvement and de-bottlenecking. The model emphasized on a data-driven approach to address process optimization and debottlenecking challenges. Industry case studies are used to demonstrate the application of the developed model in each chapter. The L&G framework has reflected an improvement of 18.25% in process improvement. Meanwhile, the debottlenecking strategy reflects the possible pathways with the consideration of operation, economic and environment to enable the industrialist to be flexible and adaptive towards the uncertainty in the global economy. The performance of the L&G model is highlighted at the end of each chapter

3D-Printed Artificial Microfish

Hydrogel microfish featuring biomimetic structures, locomotive capabilities, and functionalized nanoparticles are engineered using a rapid 3D printing platform: microscale continuous ­optical printing (μCOP). The 3D-printed ­microfish exhibit chemically powered and magnetically guided propulsion, as well as highly efficient detoxification capabilities that highlight the technical versatility of this platform for engineering advanced functional microswimmers for diverse biomedical applications

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

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

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

3D-Printed Artificial Microfish

Hydrogel microfish featuring biomimetic structures, locomotive capabilities, and functionalized nanoparticles are engineered using a rapid 3D printing platform: microscale continuous ­optical printing (μCOP). The 3D-printed ­microfish exhibit chemically powered and magnetically guided propulsion, as well as highly efficient detoxification capabilities that highlight the technical versatility of this platform for engineering advanced functional microswimmers for diverse biomedical applications

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

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