19 hasil untuk "Siming You"
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Artikel Jurnal Teks lengkap
Machine learning methods for modelling the gasification and pyrolysis of biomass and waste
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Artikel Jurnal Teks lengkap
Review of biochar production via crop residue pyrolysis: Development and perspectives
Worldwide surge in crop residue generation has necessitated developing strategies for their sustainable disposal. Pyrolysis has been widely adopted to convert crop residue into biochar with bio-oil and gas being two co-products. The review adopts a whole system philosophy and systematically…
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Artikel Jurnal
Preventing the Immense Increase in the Life-Cycle Energy and Carbon Footprints of LLM-Powered Intelligent Chatbots
Intelligent chatbots powered by large language models (LLMs) have recently been sweeping the world, with potential for a wide variety of industrial applications. Global frontier technology companies are feverishly participating in LLM-powered chatbot design and development, providing several…
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Artikel Jurnal Teks lengkap
Assessment of the renewable energy generation towards net-zero energy buildings: A review
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Artikel Jurnal Teks lengkap
Life-cycle assessment of pyrolysis processes for sustainable production of biochar from agro-residues
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Artikel Jurnal
Machine learning for sustainable organic waste treatment: a critical review
Abstract Data-driven modeling is being increasingly applied in designing and optimizing organic waste management toward greater resource circularity. This study investigates a spectrum of data-driven modeling techniques for organic treatment, encompassing neural networks, support vector machines…
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Artikel Jurnal
A machine learning model guided by physical principles for biofilter performance prediction
Abstract Despite the critical role of biofilters in water quality and sustainability, predicting their performance remains challenging due to the complexity of microbial interactions and limitations of sparse, high-dimensional datasets. Here, we introduce EnviroPiNet, a novel physics-guided AI…
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Artikel Jurnal
Machine learning toward improving the performance of membrane-based wastewater treatment: A review
Machine learning (ML) is a data-driven approach that can be applied to design, analyze, predict, and optimize a process based on existing data. Recently, ML has found its application in improving membrane separation performance for wastewater treatment. Models have been developed to predict the…
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Artikel Jurnal Teks lengkap
A critical review on biochar for enhancing biogas production from anaerobic digestion of food waste and sludge
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Artikel Jurnal Teks lengkap
Interactions between microplastics, pharmaceuticals and personal care products: Implications for vector transport
Microplastics are well known for vector transport of hydrophobic organic contaminants, and there are growing concerns regarding their potential adverse effects on ecosystems and human health. However, recent studies focussing on hydrophilic compounds, such as pharmaceuticals and personal care…
Juga tersedia di: DOAJ
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Artikel Jurnal
Machine learning–based multi-objective optimisation of low-carbon and profitable hydrogen and diesel production from non-recycled municipal plastic waste: An integrated life cycle assessment and cost–benefit analysis
Sustainable plastic waste management is essential for net zero trajectory, potentially transforming the sector from an emissions source to a circular asset. MPWs (Municipal Plastic Wastes) that are not mechanically recycled can go through pyrolysis-based chemical recycling to produce hydrogen and…
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Artikel Jurnal
Unleashing high-efficiency biohydrogen production from Arundo donax L. through interfacial charge transferring in hybrid catalytic systems by regulating metabolism
This study examines photo-fermentative biohydrogen production (PFHP) from Arundo donax L. using binary (SiC/ZnO) and ternary (SiC/ZnO/SnO2) co-catalyst systems. The maximum cumulative hydrogen production of 110 mL/g and hydrogen production rate of 39.74 mL/g·h⁻¹ at 24 h was achieved in ternary-C2…
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Artikel Jurnal
Xenon lamp weathering induced structural evolution and pyrolysis mechanisms of HDPE-ethanol processing residue composites
This study investigates how accelerated weathering influences the pyrolysis behavior of a bio-based composite prepared from high-density polyethylene (HDPE) and lignocellulosic ethanol-processing residue (EPR). The composites were subjected to xenon-lamp exposure for varying durations and…
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Artikel Jurnal Teks lengkap
Pseudomonas aeruginosa Interactions with Drinking Water Biofilm after an Acute Spike in Annular Bioreactors—Attachment, Persistence, Release, and Reattachment
The potential of DWDS pipewall biofilms to shelter and propagate opportunistic pathogens is currently poorly understood. Here, we use an annular biofilm reactor approach to quantify the fate of the opportunistic pathogen Pseudomonas aeruginosa when introduced to a simulated DWDS environment. We…
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Artikel Jurnal Teks lengkap
Modelling the Dynamics of P. aeruginosa in the Formation of Biofilms
The accumulation, growth, and re-mobilization of pathogens on the pipe walls in drinking water distribution systems are processes that affect the risk of exposure at the tap. We present a model that uses the Buckingham Pi theory to embody the physics of Pseudomonas aeruginosa accumulation and move…
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Artikel Jurnal
Smart membranes for oil/water emulsions separation: A review
Oily wastewater poses a significant impact on both environments and human societies. Especially, the treatment of oil/water emulsions for separating oil from water is challenging due to the high stability of oil/water emulsions. Smart membranes, known as stimuli-responsive membranes, are one of the…
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Artikel Jurnal
Applied machine learning for predicting the properties and carbon and phosphorus fate of pristine and engineered hydrochar
Abstract Application of advanced techniques and machine learning (ML) for designing and predicting the properties of engineered hydrochar/biochar is of great agro-environmental concern. Carbon (C) stability and phosphorus (P) availability in hydrochar (HC) are among the key limitations as they…