The growing oil and gas industry is constantly faced with the challenge of efficient management of Produced Water, a byproduct of hydrocarbon production that poses significant environmental and operational concerns, mainly due to the substantial volumes generated during extraction. Produced Water is usually contaminated with hydrocarbons, other organic substances, salts, heavy metals, and inorganic substances, and must be treated before disposal. This study explores the transformation of rice husk, an abundant agricultural waste, into biochar for its potential use in treating produced water. FARO 44 rice husk was selected based on favorable adsorption properties such as silica content, porosity, and surface functionality. Through controlled pyrolysis, 315 g of biochar was obtained from 1 kg of feedstock. Characterization using SEM revealed porous surface cavities; EDX confirmed high carbon and silica content along with ash-forming elements (Ca, Mg, K, P); and FTIR identified dominant functional groups for contaminant removal: C-H bending for hydrocarbons, Si–OH (silanol groups) for heavy metals, and O–H (including protonated –OH₂+) for inorganic ions. A batch adsorption setup was used to filter 1200 mL of produced water through the biochar, yielding 950 mL of treated water. Analytical results showed removal efficiencies of 95.9% for total hydrocarbons, 65.1% for inorganic ions, and 58.6% for heavy metals, with some metal leaching observed. These findings confirm rice husk biochar as a promising low-cost, eco-friendly treatment medium aligned with circular economy and sustainability goals.
F. Kabir, A. Salihu, A. Gimba et al.· SPE Nigeria Annual Internati...· 0 citations
Artificial Intelligence (AI)–enhanced Computer-Aided Manufacturing (CAM) is transforming advanced manufacturing, including aerospace and energy sectors, by enabling precise, efficient, and reliable production of complex components. Through a synthesis of recent literature and theoretical analysis, this study highlights how AI-driven CAM optimizes toolpaths, improves predictive maintenance, and enhances machining accuracy and structural integrity. Key advances from 2020–2025, such as digital twins, adaptive toolpath strategies, hybrid additive-subtractive processes, and smart machining environments, demonstrate reduced errors, faster production cycles, and improved integration between design and manufacturing teams. Challenges remain in data availability, implementation costs, workforce readiness, cybersecurity, and process integration. The paper contributes by providing updated insights into AI-CAM synergy and strategies for practical industrial adoption.
T. Ogedengbe, S. Ogunlowo, O. V. Eşidir et al.· SPE Nigeria Annual Internati...· 0 citations
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