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Multiscale Dynamics in Advanced Cogasification of Solid Fuels and Green Energy Carriers: Integrating Microstructural Kinetics, CFD Aerodynamics and AI-Driven GHG Mitigation
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This review paper looks at how factories can turn waste materials like coal, plants, and even plastic trash into clean-burning gas fuel using computer modeling and AI, instead of relying on more traditional fossil fuels. This matters for human health because these industrial processes can reduce air pollution and greenhouse gas emissions compared to standard fossil fuel burning, while also offering a way to responsibly dispose of plastic waste rather than letting it break down into microplastics in the environment. It's important to note this is a technical engineering summary of modeling techniques, not a study measuring direct health outcomes in people.
The transition of heavy industries toward lower carbon emission intensity requires the substitution of conventional fossil fuels with renewable and alternative feedstocks. Co-gasification provides a thermochemical pathway to convert heterogeneous blends of high-ash coal, biomass, plastic waste, and alternative energy carriers into syngas. The introduction of these varied feedstocks induces nonlinear variations in reaction kinetics, multiphase aerodynamics, and ash fusion behavior. Understanding these complex interactions is essential for optimizing reactor design, ensuring stable operation, and maximizing syngas yield while mitigating operational bottlenecks like agglomeration and slag deposition. This chapter examines advanced co-gasification systems through a comprehensive multiscale modeling framework. By integrating microstructural kinetic data, three-dimensional Computational Fluid Dynamics (CFD), and Artificial Intelligence (AI) models, this work connects microscopic particle degradation with macroscopic reactor operation. The synergistic approach allows for the accurate prediction of carbon conversion efficiencies and the spatial distribution of multiphase flows. Furthermore, the application of machine learning algorithms enhances process control by rapidly predicting syngas profiles. The chapter also synthesizes recent advancements in entrained flow and fluidized bed gasifier designs, emphasizing the simultaneous control of tar and particulate matter. It evaluates the downstream integration of gasification networks with Solid Oxide Fuel Cells (SOFCs) and green hydrogen-coupled renewable methanol synthesis to form efficient polygeneration systems. Finally, techno-economic and life cycle assessments (LCA) are presented to validate Greenhouse Gas (GHG) mitigation strategies in the power generation and cement sectors. Consequently, this integrated framework provides critical insights for the model-based design, optimization, and industrial scale-up of advanced, low-carbon gasification technologies.
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AI Driven Plastic Waste-to-fuel Conversion System for Circular Economy
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Scientists have designed a smart system that uses AI to sort plastic waste and then heat it (without oxygen) to break it down into usable fuel, instead of letting it pile up in landfills or get incinerated. This matters because reducing landfilled and burned plastic means less plastic pollution breaking down into microplastics and toxic fumes that can end up in our water, food, and lungs. While this is a proposed technology system rather than a study on health outcomes, it points toward a promising way to cut plastic waste while generating usable energy.
Artificial intelligence for plastic pyrolysis to produce fuels and chemicals: From reaction kinetics and reactor design to intelligent agent development
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This review paper looks at how artificial intelligence can help turn waste plastic into fuel and useful chemicals through a heating process called pyrolysis, which is a promising way to tackle plastic pollution instead of letting it pile up in landfills or oceans. While this technology doesn't directly address microplastics already in our bodies, reducing overall plastic waste through smarter recycling could mean less plastic breaking down into the environment over time. The paper notes AI tools are still limited by insufficient data, so this remains a developing area of research rather than a ready-to-use solution.
Pyrolysis Technology: From Heating Methods and Reactor Design to Catalytic Upgrading
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Scientists found a way to turn plant waste (like crop leftovers and wood scraps) mixed with plastic waste into a cleaner, more efficient fuel through a heat-based process called pyrolysis. This matters because it offers a practical way to recycle plastic waste that would otherwise pile up in landfills or break down into microplastics that pollute our environment and potentially end up in our food and water. While this study focuses on fuel production rather than direct health outcomes, reducing plastic waste accumulation is an important step toward limiting microplastic pollution exposure for people and ecosystems alike.
Compressive strength prediction of metakaolin mortar using CATBoost enhanced with genetic algorithm and particle swarm optimization
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This paper is actually about construction materials, not human health directly, it uses AI to predict the strength of eco-friendly concrete mixes that use metakaolin (a clay-based additive) to replace some cement, which cuts carbon emissions. The relevance to you: greener building materials like this help fight climate change, which is increasingly recognized as a public health issue tied to air quality, extreme heat, and other health risks. Note: this study doesn't address microplastics or direct health effects, it's focused on engineering and environmental sustainability.
Simulation of Microscale Combustion of a Composite Rocket Propellant
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This paper isn't actually about human health — it's a technical study on how solid rocket fuel burns at the microscopic level, looking at how factors like pressure, temperature, and the size of fuel particles affect combustion. This research is relevant to rocket engineers designing propellants, but it has no connection to microplastics or human health topics.
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