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STUDY AND DEVELOPMENT OF CYLINDRICALLY STRUCTURED POLYMER ELECTROLYTE MEMBRANE FUEL CELLS

2026-07-29T11:31:37Z

Title: STUDY AND DEVELOPMENT OF CYLINDRICALLY STRUCTURED POLYMER ELECTROLYTE MEMBRANE FUEL CELLS Authors: Sethy, Sunil Kumar Abstract: The rapid increase in energy demand and fossil fuel consumption are the major challenges in terms of environmental stability and climate change, necessitating the use of alternative energy sources. Hydrogen energy, among such sources, has emerged as an environmentally friendly alternate energy source that changes the renewable energy scenario due to its ability to lower carbon footprint and reduce dependence on fossil fuels. Polymer electrolyte membrane fuel cells (PEMFC) are one such source with a high-power density (600-2000 kW/L) and efficiency (40-50%, in CHP >90%), lower operating temperature (~70oC), ultralow operational noise, and pollution that are preferred in transportation, stationary, aviation, and other applications. Furthermore, cylindrical PEMFCs (Cy-PEMFCs) are the most effective than that of planar cells in terms of gravimetric and volumetric power density for lower-power applications. In the cell construction, the membrane electrode assembly (MEA) was wrapped between anode and cathode current collectors (CC) and further clamped together appropriately. The absence of typical bipolar plates (BPPs) makes the cell compact and lightweight, thereby outperforming the planar cells, particularly in lower-power applications (<500 W). The advantages of cylindrical design over other designs that make it a promising candidate for both low and high-power portable applications are, • Cylindrical fuel cells are cost-effective because of the absence of graphite bipolar plates. • Weight and size are less than that of the planar cell, which leads to better gravimetric and volumetric power density. • The thinner cathode current collector used in the cylindrical design facilitates easier water management. • It can operate the stack in the air-breathing mode without the shell enclosure and with the oxidant supply from the compressed storage, as in the case of conventional PEMFCs, by employing a shell enclosure. This study focused on the development of Cy-PEMFC with optimized operating parameters and improved performance. The cell was first modelled numerically for both structural and CFD modelling and validated with an ICR of 0.40 Ω-cm2, with clamping pressure of ~1.5 MPa to determine the contact pressure and cell performance, respectively. Further, considering contact resistance identified as one of the key influencing parameters, a spring clamping system was developed using spring and rotating discs. The existing strap clamping system was ii compared with the spring mechanism on the basis of species transport, contact pressure distribution, contact resistance, and cell performance. The contact pressure distribution of the spring mechanism was found to be more uniform, resulting in a performance enhancement of 15 mW/cm² owing to a 33% rise in contact pressure. Additionally, the spring system availed better breathing space for the fuel and oxidant on the anodic (~6%) as well as cathodic sides (~12%). Subsequently, the cell performance was analyzed for energy and exergy efficiency, followed by sustainability indices. The energy and exergy efficiencies increased from 16.39 % to 36.67 % and 10.93% to 30.35 % for 91.21 mA/cm2 to 400 mA/cm2, respectively. The maximum exergy efficiency was obtained as 30.35% at 2.4 A and 0.3 V. Furthermore, the environmental sustainability index for the Cy-PEMFC was increased with exergy efficiency (10.93 to 30.35 %) from 0.04 to 0.25 at optimized operating conditions (at 1 bar, 323 K). Finally, the cell underwent degradation analysis through accelerated degradation tests under different operating temperatures and RH. The potential decay rate was found to be 0.35 mV/h for the 120-h test. However, the optimized operating condition was found to be at ~35oC and RH ~60%. The future scope of the study includes extended degradation studies to support the viability of practical applications. Keywords: PEMFC, Modelling, Stack design, Performance, Contact resistance, Degradation, Exergy analysis.

MICROALGAE-MEDIATED REMOVAL OF WATERBORNE PATHOGENS FOR WASTEWATER DISINFECTION

2026-04-24T06:49:00Z

Title: MICROALGAE-MEDIATED REMOVAL OF WATERBORNE PATHOGENS FOR WASTEWATER DISINFECTION Authors: Bhatt, Ankita Abstract: Contaminated water presents an alarming global concern, with a high prevalence of pathogens in the treated or disinfected water. Amidst the global challenges of water pollution and waterborne diseases, microalgal technology has emerged as a viable alternative for wastewater treatment. The ability of microalgae to remove pathogens from wastewater has been recently brought to the limelight. However, the microalgae-mediated pathogen removal (MAPR) process is still underexplored. The present thesis entails an exhaustive analysis of MAPR, including optimization of key process parameters and bioprospecting microalgal strains for MAPR efficiency. Subsequently, an in-depth analysis has been conducted for each plausible pathogen removal mechanism in MAPR followed by reactor-scale validation studies. Finally, life cycle assessment (LCA) and quantitative microbial risk assessment (QMRA) have been employed to conduct a combined enviro-microbial assessment of MAPR. The Chlorella pyrenoidosa-mediated Escherichia coli removal from municipal wastewater was considered as the model MAPR system. A multivariate optimization technique was employed to obtain optimal MAPR conditions for maximum pathogen removal efficiency. A significantly high removal efficiency of 99.98% was obtained for C. pyrenoidosa-mediated E. coli removal under high illumination (18000 lux) and 37℃. Thereafter, a comparative MAPR demonstrated that C. pyrenoidosa was the most robust microalgal strain with efficient E. coli removal rates and beneficial biochemical characteristics. The C. pyrenoidosa-based MAPR was validated with real sewage samples whereby the microalgal process demonstrated 98% total bacteria removal, 98% Enterobacteriaceae removal, and complete removal of Salmonella sp. Along with efficient pathogen removal, the sewage-cultivated MAPR biomass demonstrated a high abundance of beneficial biomolecules, thus providing opportunities for resource recovery.

ALGAE-INTEGRATED PROCESS FOR RESOURCE RECOVERY, METABOLITE PRODUCTION, AND RECYCLING OF REVERSE OSMOSIS REJECT

2026-04-24T06:41:15Z

Title: ALGAE-INTEGRATED PROCESS FOR RESOURCE RECOVERY, METABOLITE PRODUCTION, AND RECYCLING OF REVERSE OSMOSIS REJECT Authors: Bhandari, Mamta Abstract: This study unveiled the usability of reject generated from a drinking water RO plant for algal biomass and valuable metabolite production. Initially, the RO reject collected from the domestic RO unit (ROR1), and commercial RO plants (ROR2) were investigated for algae (Chlorella pyrenoidosa) cultivation. The initial assessments revealed that the addition of some amount of BG11 (0-100%) into ROR significantly enhances biomass growth and improves the biochemical composition of C. pyrenoidosa. Notably, 25% BG11 supplementation in ROR1 increased biomass by 29.52%. Additionally, lipid content in C. pyrenoidosa grown on 50% ROR1 was nearly double that of the BG11 (positive control). RORs from various locations were further examined for algal cultivation, demonstrating the robustness of the proposed approach. A diverse range of algal strains, including C. pyrenoidosa, Scenedesmus obliquus, Chlorella sorokiniana, Scenedesmus sp., and native strains, were successfully cultivated in ROR. The highest biomass productivity for S. obliquus and the native isolate was achieved in 50% ROR, while C. sorokiniana and Scenedesmus sp. exhibited the highest lipid productivities (19.37 ± 1.04 and 18.49 ± 0.0 mg L-1 d-1, respectively) in the same medium. Furthermore, the algae demonstrated efficient nutrient removal capabilities, achieving up to 77.59% nitrate, 82.71% phosphorus, and 79.69-95.89% total dissolved solids (TDS) removal from the ROR-based growth media. Species-specific responses were observed with varying bicarbonate concentrations (0-4 g L-1) in the ROR. C. pyrenoidosa exhibited the highest biomass growth (760.81±34.24 mg L-1) at 4.0 g L-1 bicarbonate, while Scenedesmus sp. showed the maximum biomass yield (1041.81±33.32 mg L-1) in ROR without bicarbonate. Interestingly, bicarbonate in ROR promoted biofuel precursor synthesis. S. obliquus and C. sorokiniana acquired maximum carbohydrate and lipid yields at 0.5 and 1.0 g L-1 bicarbonate, respectively. Fatty acid methyl ester (FAME) analysis revealed improved biodiesel quality for C. sorokiniana, while C. pyrenoidosa was found to be rich in polyunsaturated fatty acids (PUFA). Theoretical methane potential (TMP) analyses additionally indicated enhanced biogas yield in ROR supplemented with bicarbonate.

ADVANCED DATA ANALYSIS AND MACHINE LEARNING TECHNIQUES FOR SOLAR PV FORECASTING

2026-04-13T06:36:48Z

Title: ADVANCED DATA ANALYSIS AND MACHINE LEARNING TECHNIQUES FOR SOLAR PV FORECASTING Authors: Gupta, Priya Abstract: The work in this thesis draws upon the developments in machine learning (ML) techniques and modern data analysis or decomposition methods for solar PV forecasting. Moreover, it focuses on developing decomposition-based hybrid models by balancing performance accuracy and time complexity. The primary objective of integrating decomposition techniques with ML models is to simplify their learning process by dividing complex time series data into simple subseries. One of the challenges associated with using decomposition techniques is their time-consuming nature. Following a detailed study of the relevant literature, this thesis presents various solutions in terms of the selection of ML techniques (i.e., distance-based ML, kernel-based ML, tree-based ensemble, traditional neural networks, and deep learning (DL)), utilization of dimensionality reduction methods (such as Principal Component Analysis (PCA)), and choice of the appropriate decomposition approach (i.e., Empirical Mode Decomposition (EMD) and its updated variants). The work performed in this thesis involves univariate and multivariate Global Horizontal Irradiance (GHI) forecasting, spanning across hour-ahead forecasting, hourly day-ahead forecasting, and forecasting applications in microgrid. The performance of the developed models has been tested for different Indian locations lying under four distinct climatic zones: hot-dry, composite, cold, and warm-humid. Given the scope of work, first a univariate GHI forecasting model for a forecast horizon of 1-11 h has been developed, comparing (i) traditional EMD with its updated univariate variant (EEMD), and (ii) tree-based ensembles with kernel-based ML. For a forecast horizon of 1 h, both EMD and EEMD demonstrated appropriateness; however, the latter outperformed the former with an average error reduction of 25.35 % while combining with the best-performing ML model. In contrast, with the increase in forecast horizon from 1 to 11 h, EMD didn't fit well, while EEMD exhibited superiority. Next, multivariate GHI forecasting has been explored in this thesis. For this purpose, univariate EMD and EEMD have been replaced by their multivariate versions, viz., Multivariate Empirical Mode Decomposition (MEMD) and Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD). MEMD has been combined with a stack of simple ML models (model 1) and with a combination of Principal Component Analysis (PCA) and modern Gated Recurrent Unit (model 2). For predicting an hour ahead of GHI (single-step forecasting), the average root mean square errors (RMSE) of 41.83 W/m² and 36.85 W/m² have been obtained for model 1 and model 2, respectively, across four studied locations. However, the reduced forecast error with model 2 compared to model 1 is achieved at the expense of high computational time complexity ((model 1): 535 sec and (model 2): 326 sec). This depicts a tradeoff between performance accuracy and computational time complexity for these two models. Further, the potential of decomposition-based ML/DL forecasters for multi-step (hourly day-ahead) PV power forecasting has been investigated following their assessment in single-step forecasting. This analysis demonstrated the superiority of Long Short-Term Memory (LSTM) (temporal feature extraction-based DL model) over Convolutional Neural Network (CNN) (spatial feature extraction-based DL model) and Extreme Gradient Boosting (XGBoost) (Boosting ensemble model) models. An average RMSE of 65.08 W/m² is found for hourly day-ahead PV power forecasting with the proposed NA-MEMD-LSTM model. This study also suggests replacing MEMD with NA-MEMD, as the latter consumes less time in decomposing the data while giving higher performance accuracy. As mentioned earlier, this thesis considers the time complexity of the forecasting models. A computer with specifications of a 64-bit operating system, 16 GB RAM, and an Intel Core i7-2600CPU@3.40GHz processor is used to run all the models. With the given computer specifications and for a data size of 20000 samples, the considered data analysis techniques can be arranged in ascending order of decomposition time as follows: EMD (≈ 20 sec) < EEMD (≈ 200 sec) < NA-MEMD (≈ 250 sec) < MEMD (≈ 300 sec). This thesis also examines the impact of the disparity between predicted and actual PV power generation on microgrid frequency. For the considered combinations of two forecasting models and three secondary controllers, the standard deviation (SD) of frequency is the lowest for the LSTM forecaster and Particle Swarm Optimization- Proportional Integral Derivative (PSO-PID) controller. The corresponding reduction of SD, after replacing Persistence: PSO-PID with LSTM: PSO-PID, in combination with |clear: cloudy| day is |28.43 %: 32.12 %| for overshoot and |11.87 %: 18.36 %| for undershoot frequency deviation.