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Exploring waste-derived catalysts for sustainable biodiesel production: a path towards renewable energy
(Springer Science and Business Media LLC, 2024-07-17)
T. Sathish
;
Sivamani Selvaraju
;
N. Ahalya
;
Ashok Kumar
;
Abhishek Agarwal
Fossil fuels have a high energy density, meaning they contain a significant amount of energy per unit of volume, making them efficient for energy production and transport. Biodiesel is especially becoming a fossil fuel alternative and a key part of renewable energy. Several types of waste from homes, markets, street vendors, and other industrial places were collected and transesterified with Ni-doped ZnO nanoparticles for this study. These included castor oil, coffee grounds, eggshells, vegetable oil, fruit peels, and soybean oil. The Ni-doped ZnO’s were then calcined at 800 °C. The maximum conversion rate found in converting fruit peel waste into biodiesel is about 87.6%, and it was 89.6% when the oil-to-methanal ratio was about 1:2 and the reaction time was 140 min. This is the maximum biodiesel production compared to other wastes. Moreover, using vegetable oil with nanocatalyst, the maximum biodiesel production rate of about 90.58% was recorded with 15% catalyst loading, which is the maximum biodiesel production compared with the other wastes with nanocatalyst. Furthermore, at 75 °C and a concentration of catalyst of about 15% the maximum biodiesel production obtained by using castor oil is about 92.8%. It has the highest biodiesel yield compared with the yield recorded from other waste. The catalyst also demonstrated great stability and reusability for the synthesis of biodiesel. Using waste fruit peels with Ni-doped ZnO helps to progress low-cost and ecologically friendly catalyst for sustainable biodiesel production.
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Influence of fly ash on thermo-mechanical and mechanical behavior of injection molded polypropylene matrix composites
(Elsevier BV, 2023-12)
Rajhans Meena
;
Abdul Wahab Hashmi
;
Shadab Ahmad
;
Faiz Iqbal
;
Hargovind Soni
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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.
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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.

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