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MICROALGAE-MEDIATED REMOVAL OF WATERBORNE PATHOGENS FOR WASTEWATER DISINFECTION

Wed, 01 May 2024 00:00:00 GMT

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

Wed, 01 May 2024 00:00:00 GMT

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

Wed, 01 May 2024 00:00:00 GMT

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.

DEVELOPMENT OF CONTROL STRATEGIES FOR DC MICROGRID

Thu, 01 Feb 2024 00:00:00 GMT

Title: DEVELOPMENT OF CONTROL STRATEGIES FOR DC MICROGRID Authors: Meena, Ramjee Lal Abstract: A new trend of small-scale power generation, integrated locally at the distribution voltage level, is emerging to solve the problems of climate change, energy security, and sustainable development. This power generation is known as distributed generation (DG), and the distribution system comprising DGs is called an active distribution system. Due to their intermittent power output, integrating renewable energy resources (RESs) into the utility grid and supplying local loads necessitates an energy storage system to enable a time shift between energy production and consumption. A microgrid (MG) is a cluster of DG sources, distributed storage devices, and distributed loads in which all components operate in a controlled manner to improve the reliability and quality of the local power supply and the power system as a whole. Based on their common link, MGs are classified as AC microgrid (ACMG), DC microgrid (DCMG), and AC/DC hybrid microgrid. The DCMG has gained more attention, acceptance, and popularity than the ACMG since the benefits include higher reliability, higher power quality, higher efficiency, low cost, absence of frequency and reactive power control, and more straightforward analysis and design of control loops. From an operation point of view, there are three main issues to be addressed in DCMG: (i) harnessing maximum DG/SPV power generation, (ii) effective use of energy storage system, and (iii) proper regulation of DC bus voltage. These are interrelated control issues. The MG can operate as a controllable coordinated module in an On-grid or Off-grid mode. In DCMG, DC voltage is the unique quantity controlled by injecting or absorbing power. Less research is reported for short-term source disturbance, fast load fluctuation, and nonlinear load in both Off-grid and On-grid operations. These situations impose a high-frequency transient current in the DCMG that is not shared appropriately among sources and storage by droop control. Thus, a circulating current flows among sources and storage. It becomes the cause of concern for power quality, reliability, stress on storage, and efficiency of converters. AC side fault and its impact on AC and DC side are also not reported in the literature in the presence of high-power density storage on DC bus. In this thesis, a novel control strategy is developed to address these problems with a composite energy storage system (CESS).