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Item type:Publication, Experimental thermodynamic investigation and hybrid RSM-ANN prediction of hydrogen-enriched algal biodiesel combustion(Elsevier B.V., 2026-12-01)The growing demand for low-carbon and high-efficiency combustion systems has accelerated research on renewable fuels compatible with existing compression ignition (CI) engines. This study experimentally investigated the combustion, performance and emission characteristics of a single-cylinder diesel engine fuelled with hydrogen-enriched Algal Oil Methyl Ester (AOME) using a hybrid Response Surface Methodology (RSM) and Artificial Neural Network (ANN) approach. Engine load (0 - 100%), injection pressure (200 - 240 bar), and hydrogen flow rate (3 - 9 LPM) were selected as the primary input parameters. A total of 45 experimental runs were conducted to evaluate peak cylinder pressure, heat release rate (HRR), brake thermal efficiency (BTE), brake specific fuel consumption (BSFC) and exhaust emissions. Results indicated that hydrogen enrichment significantly enhanced combustion characteristics, with peak cylinder pressure increasing from 34.75 to 79 bar and HRR rising from 29.72 to 157.62 J/ °CA at full load conditions. Maximum BTE of 44.01% and minimum BSFC of 0.141 kg/kWh were achieved under optimized conditions. Hydrogen addition also reduced CO, HC and smoke emissions by 91%, 90% and 99%, correspondingly, the NOₓ emissions increased at higher loads due to elevated combustion temperatures. The ANN model outperformed RSM, achieving prediction accuracy with R² values exceeding 0.98. Multi-objective optimization produced a desirability value of 0.958. The results prove the potential of hydrogen-enriched AOME dual-fuel operation for sustainable automotive and long-duration CI engine applications aligned with global net-zero and SDG goals.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, EEG and EMG Signal Analysis for the Early Detection of Parkinson's Disease(Engineering, Technology & Applied Science Research, 2026-06-06)Parkinson's Disease (PD) is a progressive neurodegenerative disorder that significantly impacts motor and cognitive function. Early and accurate diagnosis is a significant clinical challenge. This study proposes a hybrid deep learning framework that integrates Electroencephalography (EEG) and Electromyography (EMG) signals to classify PD patients. EEG signals were collected using the Emotiv Epoc X headset (14 channels, 10–20 system). At the same time, EMG data were acquired from three sensors placed according to the SENIAM standard (biceps, flexor carpi, extensor digitorum). Publicly available datasets, including the San Diego PD EEG dataset, were employed for model training and evaluation. Preprocessing included 1–50 Hz band pass filtering, Independent Component Analysis (ICA) for artifact removal, and epoch segmentation for EEG, while EMG signals underwent 20–450 Hz filtering, rectification, and RMS smoothing. A hybrid Convolutional Neural Network (CNN)– Long Short-Term Memory (LSTM) architecture was developed in Python to capture spatial and temporal dependencies in the multimodal bio signals. The model achieved 99% classification accuracy with a training loss of 0.14, demonstrating strong predictive power for early-stage PD detection. Despite promising results, the study is limited by the use of only 14 EEG electrodes and three EMG electrodes, with recordings restricted to rest conditions. Future work will expand electrode coverage, incorporate additional limb-based EMG sensors, and evaluate PD-related neural and muscular activity during more diverse tasks, such as puzzle solving, handwriting, and typing. This research highlights the potential of multimodal deep learning approaches for early and non-invasive PD diagnosis. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Clean combustion optimization of hydrogen-algae biodiesel mixtures for performance-emission trade-offs using response surface methodology(2026-07-17)The research investigation of RSM optimization to analyze the impact of load, Algae Biodiesel blend and Hydrogen concentration on engine performance and exhaust gas emissions in diesel engine produces 5.2 kW at 1500 rpm. The testing process involved injecting B20 biodiesel blend into the combustion cylinder and introducing hydrogen complete the intake manifold. The objective of this study investigates the effect of engine load, algae biodiesel blend and hydrogen flow rate on the performance, combustion and emission characteristics of a diesel engine using RSM for optimization. The experimental results reveal, the BTE will be enhanced by an increase in hydrogen concentration in the B20 compound. In comparison to the base B20, the BTE was increased by 29.89% by the mixture B20 with 9 LPM, resulting in a 18.8% improvement. In the same, this concentration demonstrates 25.38% decrease in BSFC when contrasted with the B20 combination. At Higher concentration of hydrogen, CO and HC emissions decreases 20% and 10.11% respectively, while NOx emissions increase by 36% and smoke opacity decreased by 17% respectively. The engine parameters were optimised using RSM with Central Composite Design and a desirability function. The analysis determined that the most effective combination was B20 biodiesel blended with 9 LPM hydrogen. Among the evaluated conditions for CI engine operation, this blend attained the highest overall desirability. The research is directly support to Sustainable Development Goals, SDG 7 - Affordable and Clean Energy and SDG 13- Climate Action, by promoting cleaner, renewable and low-emission energy solutions for future transportation.