Research Article
Sustainable Porous Carbon from Ziziphus Fruit Waste for High Specific Capacity Electrode Materials
Hamouda Adam Hamouda*,
Inaam Ali Salim,
Abdelwahab Abuelgasim Mohammed Adam,
Taysir Abdrhman Musa,
Elsadig Omer Fadul
Issue:
Volume 12, Issue 3, June 2026
Pages:
42-48
Received:
23 June 2026
Accepted:
6 July 2026
Published:
24 July 2026
DOI:
10.11648/j.ajme.20261203.11
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Abstract: The growing demand for efficient energy storage for renewable systems requires low-cost, high-performance electrode materials. Supercapacitors offer fast charge-discharge but suffer from low energy density. Biomass-derived porous carbon is a sustainable alternative to commercial activated carbon. This study aims to synthesize hierarchical N,O-doped porous carbon from Ziziphus spina-christi fruit waste in order to evaluate the effect of alkali agent and activation time on porosity and supercapacitor performance. The carbon was prepared via carbonization at 600°C followed by chemical activation with KOH or NaOH at 800°C for 2 h and 3 h. The materials were characterized by FE-SEM, XRD, Raman, and N2 adsorption-desorption. The electrochemical tests were conducted in 3-electrode system. The optimized ZSCFC-3h-KOH exhibited a high specific surface area of 917.5 m2 g-1, a hierarchical micro-mesoporous structure, and an ID/IG ratio of 0.98. In a 3-electrode system, it delivered a specific capacitance of 231.6 F g-1 at 1 A g-1 with an IR drop of only 0.03 V. The electrode showed excellent stability, retaining 94.3% of its capacitance after 10,000 cycles at 5 A g-1. Z. spina-christi fruit waste is a viable, sustainable precursor for high-capacitance supercapacitor electrodes. KOH activation for 3 h is optimal for creating accessible pore networks for ion storage.
Abstract: The growing demand for efficient energy storage for renewable systems requires low-cost, high-performance electrode materials. Supercapacitors offer fast charge-discharge but suffer from low energy density. Biomass-derived porous carbon is a sustainable alternative to commercial activated carbon. This study aims to synthesize hierarchical N,O-doped...
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Research Article
Hybrid Physics-Informed Dynamic Productivity Index
(HPD-PI) Model for Oil Reservoir Production Forecasting
Issue:
Volume 12, Issue 3, June 2026
Pages:
49-58
Received:
6 April 2026
Accepted:
15 April 2026
Published:
27 July 2026
DOI:
10.11648/j.ajme.20261203.12
Downloads:
Views:
Abstract: Accurate prediction of reservoir production remains a critical challenge due to the complex interplay of multiphase flow behavior, temporal dynamics, and reservoir heterogeneity. This study proposes a Hybrid Physics-Informed Dynamic Productivity Index (HPD-PI) model that integrates classical inflow performance relationships with data-driven temporal features to enhance production forecasting. The model extends the conventional productivity index formulation by incorporating water-cut and gas-oil ratio corrections, dynamic production trends, and temporal memory through lagged production terms. A comprehensive dataset comprising production, pressure, and multiphase-flow parameters was used to evaluate the proposed approach. A baseline Random Forest model was first developed for comparison, achieving a coefficient of determination (R2) of 0.8138. However, analysis revealed that the model was predominantly driven by temporal dependencies with limited physical interpretability. In contrast, the proposed Hybrid HPD-PI model achieved superior performance, with an R2 of 0.9466, root mean square error (RMSE) of 71.01, and mean absolute error (MAE) of 38.78. The improvement is attributed to the integration of temporal memory, multiphase flow corrections, and dynamic behavior within a physically consistent framework. The results demonstrate that the hybrid model effectively captures both short-term fluctuations and long-term production trends while maintaining interpretability. The study highlights the importance of combining physical principles with data-driven techniques for robust reservoir modeling. The proposed HPD-PI framework provides a reliable tool for production forecasting, reservoir management, and decision-making, with potential applicability to a wide range of reservoir systems.
Abstract: Accurate prediction of reservoir production remains a critical challenge due to the complex interplay of multiphase flow behavior, temporal dynamics, and reservoir heterogeneity. This study proposes a Hybrid Physics-Informed Dynamic Productivity Index (HPD-PI) model that integrates classical inflow performance relationships with data-driven tempora...
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,
Adewale Usman Oguntola
