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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Peious, Sijo Arakkal Kaushik, Minakshi Shah, Syed Attique Sharma, Rahul Suran, Shweta Draheim, Dirk Information Systems Group Tallinn University of Technology Tallinn Estonia School of Computing and Digital Technology—CDT Birmingham City University Birmingham United Kingdom The Open University Walton Hall Milton Keynes United Kingdom Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9789819766833

Currently, we witness a significantly increased understanding, that disaggregation of data is the key to better-informed decision-making and enactment. The UN 2030 Agenda for Sustainable Development prominently stresses the importance of disaggregated data for achieving its goals. We argue that the key benefit of data disaggregation is the increased potential to understand confounding effects. Unfortunately, in today’s tool landscape, it lacks systematic support for understanding confounders. Therefore, in this paper, we contribute as follows. We integrate a means of confounder adjustment, i.e. multiple linear regression adjustment, as a new feature into the data analysis tool GrandReport, which works in mixed mode, i.e. for both numerical and categorical data in parallel. Next, we conduct experiments on an extended air pollutants dataset from several cities. We utilize the tool GrandReportl to investigate the correlation between standardized air quality indices and CO2 levels before and after confounder adjustment in regard with additional city data. We argue that the experiments provide evidence for the usefulness and usability of the suggested approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multiple linear regression

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Co-pyrolysis of Chlorella vulgaris with plastic wastes: Thermal degradation, kinetics and Progressive Depth Swarm-Evolution (PDSE) neural network-based optimization

Green Technologies and Sustainability

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Green Technologies and Sustainability 2024年第2期2卷

作者： Tan, Isabel Jia Yen Loy, Adrian Chun Minh Chin, Bridgid Lai Fui Cheah, Kin Wai Teng, Sin Yong How, Bing Shen Alhazmi, Hatem Leong, Wei Dong Lim, Huei Yeong Lam, Man Kee Lam, Su Shiung Department of Chemical and Energy Engineering Faculty of Engineering and Science Curtin University Malaysia CDT 250 Miri Sarawak 98009 Malaysia Chemical Engineering Department Monash University Victoria 3180 Australia HICoE-Centre for Biofuel and Biochemical Research Institute of Self-Sustainable Building Department of Chemical Engineering Universiti Teknologi PETRONAS Perak Darul Ridzuan Bandar Seri Iskandar 32610 Malaysia School of Computing Engineering and Digital Technologies Teesside University Middlesbrough TS1 3BX United Kingdom Radboud University Institute for Molecules and Materials P.O. Box 9010 Nijmegen 6500 GL Netherlands Biomass Waste-to-Wealth Special Interest Group Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching 93350 Malaysia Carbon Management Technologies Institute (CMTI) King Abdulaziz City for Science and Technology (KACST) P.O. Box 6086 Riyadh 11442 Saudi Arabia Duriane Professionals Sdn Bhd 26-1 & 26-3 Jalan Puteri 2/4 Bandar Puteri Selangor Puchong 47100 Malaysia Higher Institution Centre of Excellence (HICoE) Institute of Tropical Aquaculture and Fisheries (AKUATROP) Universiti Malaysia Terengganu Terengganu Kuala Nerus 21030 Malaysia Energy and Environment Research Cluster Faculty of Engineering and Science Curtin University Malaysia CDT 250 Sarawak Miri 98009 Malaysia Sustainability Cluster School of Engineering University of Petroleum & Energy Studies Uttarakhand Dehradun 248007 India

The search of sustainable route for biofuel production from renewable biomass have garnered wide interest to seek for various routes without compromising the environment. Co-pyrolysis emerges as a promising thermochemical route that can improve the pyrolysis output from simultaneously processing more than two feedstocks in an inert atmosphere. This paper focuses on the kinetic modeling and neuro-evolution optimization in the application of catalytic co-pyrolysis of microalgae and plastic waste using HZSM-5 supported on limestone (HZSM-5/LS), in which co-pyrolysis of binary mixture of microalgae and plastic wastes (i.e. High-Density Polyethylene and Low-Density Polyethylene) was investigated over different heating rates. The results have shown a positive synergistic effect between the microalgae and polyethylene in which the apparent activation energies values have reduced significantly (∼20 kJ/mol) compared to that obtained by pyrolysis of individual microalgae component. The kinetic models reflect that the mixture of microalgae and Low-Density Polyethylene for use as co-pyrolysis feedstock requires activation energy that is 23% and 13% lower compared to that required by pure microalgae and the mixture of microalgae and High-Density Polyethylene, respectively. The Progressive Depth Swarm-Evolution (PDSE) was used for neural architecture search, which subsequently provided optimal reaction condition at 873 K can achieve 99.6 % of degradation rate using a tri-combination of LDPE (0.13 %) + HDPE (0.77 %) + MA (0.11 %) in the presence of HZSM-5/LS catalyst. © 2024 The Authors

关键词： Co-pyrolysis Distributed activation energy model (DAEM) Kinetic analysis Microalgae Neuro-evolution Plastic waste

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