CRIS

Permanent URI for this communityhttps://cris.ute.edu.ec/handle/123456789/1

Browse

Search Results

Now showing 1 - 7 of 7

Some of the metrics are blocked by your
consent settings
Evaluation of the economic and technological aspects of producing blue hydrogen via ethanol-steam reforming with carbon capture
(Elsevier BV, 2025-12)
Pali Rosha
;
Feysal M. Ali
;
Mohammad Yusuf
;
Hussameldin Ibrahim
An industrially relevant method for obtaining hydrogen from hydrocarbons without emitting carbon into the atmosphere involves ethanol-steam reforming followed by carbon capture. Herein, we present a detailed conceptual process using ethanol-stream reforming to produce blue hydrogen, integrated with a carbon capture plant, followed by a techno-economic analysis. In the first step, the Aspen plus-based simulation of ethanol-stream reforming reactions is performed to optimize the reforming reactor geometrical parameters for a 10 t/day of hydrogen production. Afterward, the carbon capture system was designed with a standalone absorber and stripper, which were subsequently integrated for solvent makeup calculation. Considering the target value of hydrogen production, the optimized reactor diameter and length were found to be 0.18 and 2 m, respectively, corresponding to reactant flow (200 t/day) and heat duty (3.14 MW) at optimal circumstances. Absorber and stripper packing heights of 12.2 m and 5 m, respectively, with column diameters of 1.22 m and 2.60 m are required to extract 95 % CO2 from the reformed product stream. The techno-economic analysis indicates that the cost of producing one kilogram of H2 is $3.5. The computed internal rate of return is 16.6 %, the discounted payback period is 6 years, and the net present value is $13 million.
Some of the metrics are blocked by your
consent settings
Sustainable Groundwater Management in Water-Scarce Regions: A Spatial Machine Learning Analysis from Rajshahi, Bangladesh
(Bilingual Publishing Group, 2025-08-12)
Sumaya Tabassum
;
Likhon Chandra Roy
;
Amit Kumar Sarkar
;
Yassine Ezaier
;
Hader Ahmed
Ensuring the availability and sustainable management of water (SDG 6) is particularly challenging in dry regions like Rajshahi, Bangladesh, where communities rely heavily on groundwater with limited recharge potential. Issues such as declining water levels and contamination by iron, arsenic, and chloride compromise both user satisfaction and public health. This study aimed to assess groundwater quality risks through regional mapping to guide the installation depth of new water sources. In collaboration with the Department of Public Health Engineering (DPHE), data were collected from 7,388 tube wells across nine upazilas, including well depth, geographic coordinates, and contaminant concentrations. Water quality was evaluated against World Health Organization and Bangladesh standards. Machine learning (XGBoost) and spatial analysis were applied to model contaminant levels based on location and well depth. An initial model showed poor performance, but after identifying and correcting key errors, the refined model yielded significant improvements: R² increased from 0.0345 to 0.62 for iron, from −0.0015 to 0.38 for arsenic, and from 0.12 to 0.71 for chloride. A comprehensive water quality risk map was developed by integrating these results at the upazila level. This map provides actionable insights for government agencies and NGOs to prioritize areas for water quality testing, remediation, and public awareness initiatives, contributing to more informed and sustainable water resource management in the region.
Some of the metrics are blocked by your
consent settings
A comprehensive review on arsenic contamination in groundwater: Sources, detection, mitigation strategies and cost analysis
(Elsevier BV, 2025-01-15)
Mohd Wajahat Sultan
;
Fazil Qureshi
;
Salman Ahmed
;
HESAM, KAMYAB
;
Saravanan Rajendran
While groundwater is commonly perceived as safe, the excessive presence of trace metals, particularly arsenic (As), can pose significant health hazards. This review examines the current scenario of pollutants and their mitigations focusing on As contamination in groundwater across multiple nations, with a specific emphasis on the Indian Peninsula. Arsenic pollution surpasses the WHO limit of 10 ppb in 107 countries, impacting around 230 million people worldwide, with a substantial portion in Asia, including 20 states and four union territories in India. Analysis of the correlation between the aquifer and arsenic poisoning highlights severe contamination in groundwater originating from loose sedimentary aquifer strata, particularly in recently formed mountain ranges with geological sources presumed to contribute over 90% of arsenic pollution, i.e. a big environmental challenge. A myriad of techniques, including chromatographic, electrochemical, biological, spectroscopic, and colorimetric methods among others, are available for the detection and removal of arsenic from groundwater. Removal strategies encompass a wide array of approaches such as bioremediation, adsorption, coagulation/flocculation, ion exchange, biological processes, membrane treatment, and oxidation techniques specifically tailored for affected areas. Constructed wetlands help to eliminate heavy metal impurities such as As, Zn, Cd, Cu, Ni, Fe, and Cr. Their efficiency is influenced by design and environmental factors. Nanotechnology and nanoparticles have recently been studied to remove arsenic and toxic metal ions from water. Cost-effective solutions including community-based mitigation initiatives, alongside policy and regulatory frameworks addressing arsenic contamination, are essential considerations.
Some of the metrics are blocked by your
consent settings
Advancements in sorption-based materials for hydrogen storage and utilization: A comprehensive review
(Elsevier BV, 2024-11)
Fazil Qureshi
;
Mohammad Yusuf
;
Salman Ahmed
;
Moinul Haq
;
Alhafez M. Alraih
With its remarkable energy density and eco-friendly combustion properties, hydrogen stands as a beacon of hope in our quest to meet future energy needs while ushering in a cleaner, carbon-free era, making a significant impact on the path to a sustainable world. Nevertheless, the broader utilization of H2 faces hurdles concerning its generation, storage, and efficient utilization. Solid materials offer a promising avenue to address these challenges, as their properties can be readily tailored to enhance the efficiency of H2 generation, storage, and utilization. By manipulating their physical, chemical, thermal, and electronic attributes, solid materials can make substantial contributions across all three crucial aspects. Materials based on metal and complex hydrides show promise as hydrogen storage materials. The activation energy for hydrogen desorption is significantly reduced by transition metals doping, improving the materials' capacity to store hydrogen. Bimetallic nanoparticles of transition metals had outstanding catalytic and synergistic effects on the hydrogen adsorption/desorption properties of MgH2 when compared to the case of a single transition metals. Zeolites are superior to metal-organic frameworks due to their simplicity in synthesis, low thermal stabilities, and inexpensive cost. In general, hydrogen hydrates show promise as materials for hydrogen storage, but additional study is required to increase their hydrogen storage volumes, charging speeds, and cycle capabilities. Glass structure factors, such as the connectedness of the regional network, have a role in establishing the hydrogen permeabilities of glasses. The main limitations of these systems are their low volumetric hydrogen storage densities (<20 kg/m3) and the requirement for heating to liberate hydrogen. It's remarkable that organo-transition metal complexes materials showed strong 8.9 and 9.9 wt% hydrogen adsorption capabilities. Such endeavours are imperative to usher in a sustainable H2 powered future. This comprehensive review explores various materials for physisorption and chemisorption-based hydrogen storage, providing in-depth insights and pertinent comparisons to highlight their potential for effective hydrogen storage solutions
Some of the metrics are blocked by your
consent settings
Response surface optimization and support vector regression modeling of microwave-assisted essential oil extraction from cumin seeds
(Elsevier BV, 2024-02)
Ali Asif Khan
;
Sadaf Zaidi
;
Fazil Qureshi
;
Mohammad Yusuf
;
Abdullah A. Al-Kahtani
The current research involved creating models using Response Surface Methodology (RSM) and Support Vector Regression (SVR) to forecast the amount of extractable essential oil that can be obtained from powdered cumin seeds. Influence of microwave power (140–280–420–560–700 W), amount of water (500–600–700–800–900 ml), duration of distillation (30–45–60–75–90 min) and soak time (15–30–45–60–75 min) on essential oil yield were investigated. Microwave Assisted Extraction (MAE) allowed higher recoveries compared to conventional Soxhlet extraction, without altering the chemical components of the extract. A five-level four FCC experimental design was developed using Minitab (15.1.20.0). A total of 31 runs were performed in microwave-assisted extraction apparatus. Experimental data obtained was then used for developing RSM and SVR models for the prediction of the yield of essential oil. The optimum conditions for maximum yield of cumin oil were given by RSM. Maximum yield of 3.4 ml (0.017 ml/g) was found at 140 W of microwave power, 500 ml of water, 90 min duration of distillation, and 15 min of soak time. In this work, epsilon SVR with RBF kernel was used. The grid search (depth-first search) methodology was applied for tuning the values of epsilon, gamma, and cost using the LIBSVM module on the MATLAB interface. The statistical parameters namely, average absolute relative error (AARE), coefficient of determination (R2), standard deviation (SD), and root mean square error (RMSE) were selected as the performance parameters. The developed SVR model was compared with the RSM model. The AARE values of 2.27% and 1.29%, R2 values of 0.86 and 0.99, SD values of 1.73 and 0.29, and RMSE values of 0.0284 and 0.0132 were obtained for RSM and SVR models respectively. It is found that SVR is more accurate and better tool for modeling of MAE process.
Some of the metrics are blocked by your
consent settings
A review of biomass ash related problems: Mechanism, solution, and outlook
(Elsevier BV, 2024-02)
Kunmi Joshua Abioye
;
Noorfidza Yub Harun
;
Suriati Sufian
;
Mohammad Yusuf
;
Ahmad Hussaini Jagaba
The smooth combustion process of biomass is of great importance as the world transit away from the use of fossil fuels to embracing environmentally friendly and renewable energy. Ash-related problems, such as slagging, fouling, agglomeration, and corrosion, significantly disrupt the efficient operation of gasification systems, leading to unforeseen breakdowns. Potassium, a prevalent alkali metal in biomass, reacts with chlorine and sulfur in flue gas, generating troublesome compounds. This interaction constitutes the primary source of challenges linked to biomass combustion processes. Additives, especially kaolin, an aluminum-silicate, proved most effective in capturing potassium during combustion, forming high-melting-point potassium aluminum silicate compounds. Hence, this paper provides a summary of issues related to biomass ash, explores the deposition mechanism, outlines methods to mitigate problems associated with ash, and concludes by introducing alum sludge as an outlook.
Some of the metrics are blocked by your
consent settings
Response surface methodology and artificial neural network modelling of palm oil decanter cake and alum sludge co-gasification for syngas (CO+H2) production
(Elsevier BV, 2024-09)
Kunmi Joshua Abioye
;
Noorfidza Yub Harun
;
Ushtar Arshad
;
Suriati Sufian
;
Mohammad Yusuf
Syngas (CO + H2) production through biomass gasification offers a promising and sustainable alternative to conventional fuels. This study investigates the co-gasification of palm oil decanter cake (PODC) and Alum Sludge (AS), utilizing response surface methodology (RSM) and artificial neural network (ANN) techniques to optimize and predict syngas production. Conducted in a fixed bed horizontal reactor, the experiment investigates temperature, airflow rate, and particle size as input parameters. Results revealed that optimal condition of 900 °C temperature, 10 mL/min airflow rate, and 2 mm particle size yielded the highest syngas production at 39.48 vol%. The RSM showed an R2 value of 0.9896, whereas ANN network revealed an overall R2 value of 0.971. Both models demonstrated strong alignment with experimental data and the modelled equation. This research demonstrates the effective use of statistical modelling to enhance the efficiency and effectiveness of syngas production, thereby fostering advancements in sustainable energy production.

CRIS

Browse

Filters

Settings

Sort By

Results per page

Search Results