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@article{188797,
        author = {Prathamesh Sudhir Dixit and Pranav Rajkumar Pawar and Aniket Shripad Narayankar and Shailesh Vasant Andhale and Pranav Parmeshwar Navale and Pravin Savkar Bhuse and Aryan Kantilal Kolekar and Rohit Udhhav Tambave},
        title = {AI-Integrated Mechanical Processes for Carbon Recovery from Industrial and E-Waste},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {3800-3808},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188797},
        abstract = {The rapid expansion of industrial production and electronic device consumption has resulted in a continuous rise of carbon-rich waste streams, such as spent dry cells, battery residues, composite scrap, and carbon-laden e-waste components. These waste materials contain substantial fractions of recoverable carbon, which can be upgraded into value-added adsorbents or functional carbon materials. However, conventional recovery processes primarily based on fixed mechanical operations, thermal treatment, and chemical activation are highly sensitive to variations in feedstock composition. As a result, they often exhibit inconsistent product quality, excessive energy usage, and limited adaptability to heterogeneous waste inputs. To overcome these limitations, this study presents a comprehensive AI-integrated mechanical processing framework designed to automate, optimize, and stabilize carbon recovery operations.The proposed system employs a network of real-time sensors, including computer vision modules, hyperspectral probes, and embedded process instrumentation, to quantify material heterogeneity at the point of entry. Machine-learning models then interpret these multimodal signals to predict optimal shredding intensity, carbonization temperature, activation dosage, and residence time. Closed-loop control systems, reinforced by model predictive control and reinforcement learning strategies, dynamically adjust processing conditions to ensure consistent pore development, increased BET surface area, and superior carbon yield. A digital-twin environment enables virtual experimentation, predictive maintenance, and multi-objective optimization with significantly reduced operational risk. Preliminary evaluations indicate substantial improvement in energy efficiency, product uniformity, and throughput compared with traditional static-parameter methods
The increasing accumulation of industrial residues and electronic waste has intensified the challenge of recovering carbon-rich materials that would otherwise contribute to environmental degradation. Conventional recycling and treatment methods rely on static mechanical and thermal operations, which struggle to manage highly heterogeneous waste streams and typically generate inconsistent carbon quality. To address these limitations, this study proposes an integrated framework that combines advanced mechanical processing with artificial intelligence to achieve precise, adaptable, and energy-efficient carbon recovery. The methodology incorporates a sequence of mechanical and thermo-chemical operations, including shredding, controlled grinding, particle-size classification, thermal decomposition, and activation. Each stage is instrumented with multisensor modules optical, spectral, and thermal to characterize feedstock properties in real time. These signals are processed using machine-learning models that predict optimal operational settings, while predictive algorithms and optimization routines dynamically regulate reactor temperature, residence time, activation dosage, and grinding intensity.
Experimental evaluations were conducted using mixed industrial and e-waste samples subjected to stepwise thermal carbonization and chemical or steam activation. AI-driven control resulted in measurable improvements, including enhanced carbon yield, increased purity through more effective contaminant removal, and significant gains in surface area due to better pore development. The adaptive control strategy further reduced energy consumption by avoiding unnecessary thermal exposure. The outcomes demonstrate that AI-assisted mechanical systems can substantially elevate the consistency, efficiency, and sustainability of carbon recovery processes. The proposed approach establishes a scalable and intelligent pathway for converting complex waste streams into high value carbon materials, offering strong potential for next-generation waste-to-resource technologies.},
        keywords = {Artificial Intelligence, Carbon Recovery, Industrial Waste Processing, E-Waste Recycling, Mechanical Separation Systems, Machine Learning–Driven Optimization, Thermal Carbonization, Chemical Activation, Multimodal Sensing, Computer Vision for Waste Classification, Reinforcement Learning Control, Model Predictive Control (MPC), Digital Twin Simulation, Process Automation, Sustainable Waste-to-Resource Technologies},
        month = {December},
        }