http://localhost:8080/xmlui/handle/123456789/382 Sun, 26 Apr 2026 08:14:36 GMT 2026-04-26T08:14:36Z http://localhost:8080/xmlui/handle/123456789/3471 Title: DEVELOPMENT OF AN EFFECTIVE MULTI-OBJECTIVE OPTIMIZATION STRATEGY FOR THE MANUFACTURING OF RESIN TRANSFER MOULDED COMPOSITE PARTS Authors: DNYANBA, ZADE ANITA Abstract: The resin transfer moulding (RTM) technique is a widely used liquid composite moulding (LCM) process due to its advantages of uniform thickness and good surface finish for manufacturing complex composite parts. However, the RTM process is not practised widely due to the cost involved in developing mould design and process parameters. A proper mould design requires designing an effective injection strategy which contains the least number and appropriate positions of injection ports and vents that result in minimum mould filling time without dry spot content. As well, the judicious choice of mould heating time-temperature cycle is required for the effective curing process. In addition, the cognition on resin gelation-cure kinetics-rheokinetics and reinforcement mat permeabilities are the essentially required material parameters for the successful development of the RTM process. On the contrary, RTM being the closed moulding process, it is difficult to visualize resin flow and sense resin curing. Therefore, it becomes a hard task to analyse influential mould fill and cure process parameters through experimental trials and thus, the development of composite parts via the RTM process is confined. To address these challenges, this research proposes a simulation-based optimization framework utilizing supervised learning algorithms to automate and optimize the RTM process. In this framework, simulation packages are coupled with optimization algorithms to autonomously determine optimal design and process parameters. The study introduces a robust and cost-effective methodology to simulate and optimize RTM mould-filling and curing processes using an in-house coded multi-objective optimization (MOO) algorithm integrated with process simulation via multi-phase porous flow, transient heat transfer and resin cure kinetics models. This framework was implemented using COMSOL Livelink for MATLAB focusing on manufacturing a vinyl ester-glass fiber-reinforced automotive bonnet and an RTM6-carbon fiber-reinforced aircraft wing flap. Initially, vinyl ester and RTM6 resins were thermally characterized to develop the cure process windows through which the appropriate time-temperature cure cycles were identified for the curing of composite parts. From the thermal characterization of neat resins, the modified Kamal and Sourour model was effectively fitted to the experimental data of the degree of cure versus the rate of cure for both vinyl ester and RTM6 resins. Subsequently, the permeabilities of reinforcement fibre mats were measured using mould-filling experiments for their applicability in the mould-filling simulations. The effective permeability of 2.0×10-9 m2 and v 1.0×10-9 m2 were obtained using the mould-filling experiments for woven roving glass and carbon fibre mats, respectively. In the mould-filling phase, novel in-house coded Multi-Objective Stochastic-Optimization (MOSO) and Non-dominated Sorting Differential Evolution (NSDE) algorithms were developed and implemented to optimize the mould-fill phase. The NSDE algorithm was implemented for simultaneous optimization of two objectives namely, dry spot content and mould-fill time by changing the locations of gates and vents at the fixed input numbers of gates and vents. Consecutively, the MOSO algorithm was implemented for simultaneous optimization of three objectives namely, dry spot content, mould-fill time and total number of ports by simultaneously changing both the numbers as well as locations of gates and vents. The effect of race-tracking was also investigated using higher permeability values at the composite part-cut edges. Then, the efficacy of the proposed algorithms was examined with the trial and error process model simulations. From the comparative assessment, the trial and error process required more iterations with trials in numbering and positioning ports and manual efforts for obtaining a single optimal solution. Conversely, the MOO algorithms were automated and needed less manual effort and problem-specific experience to obtain the number of Pareto optimal solutions. In comparison to the NSDE algorithm, the MOSO algorithm predicted less dry-spot content, number of ports, mould-fill time and uniform resin flow-front progressions with lesser functional evaluations and computational time. In the curing phase, a novel in-house coded NSDE algorithm was developed and implemented for the simultaneous minimization of composite part thermal gradients and cure process time for both the studied composite parts. The efficacy of the proposed algorithm was examined with the in-house coded non-dominated sorting genetic algorithm (NSGA-II) and trial-error process simulations in terms of a thermal gradient, cure-time, and cure progression at the applied temperature cycles. From the results, the NSDE algorithm was found to be effective in achieving faster convergence with less cure process and computational time when compared to the NSGA-II algorithm. The NSDE algorithm performed effectively in terms of thermal gradient and cure-time with the automated predictions of the mould heating parameters when compared with the trial-error process for both the composite parts. This research significantly contributes to the field by introducing efficient and automated optimization algorithms for RTM composite parts by enhancing both manufacturing precision and time efficiency. Description: NITW Mon, 01 Jan 2024 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3471 2024-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3470 Title: DESIGN AND PERFORMANCE EVALUATION OF ADVANCED CONTROL STRATEGIES FOR BIOLOGICAL WASTEWATER TREATMENT PLANTS Authors: DEY, INDRANIL Abstract: The utilization of biological processes to treat polluted wastewater has become prevalent, encompassing both conventional domestic and industrial wastewater. This approach aims to eliminate nutrients, specifically carbon, nitrogen, and phosphorus, while adhering to regulatory guidelines for reducing nutrient discharge into surface water, as mandated by municipal water directives. There is a growing interest for enhancing the effluent quality (EQ) of sewage wastewater treatment facilities. Wastewater treatment plants are complex, nonlinear, and slow processes. The lack of adequate instrumentation, stringent environmental regulations, and the need for cost-effective plants have highlighted the significance of automating wastewater treatment processes. However, the process's complexity makes it difficult to successfully implement control systems. The main challenge is developing a control strategy that reduces operational costs (OC) while also improving EQ. This study looks into the development of various control strategies to meet these challenges. The Benchmark Simulation Model No. 1-P (BSM1-P) and Sequencing Batch Reactor (SBR) serve as test platforms for these control strategies. The primary goal is to prevent violations in effluent ammonia, total nitrogen, and total phosphorus levels while reducing operational costs and improving effluent quality. The proposed control strategies use proportional integral (PI), fractional PI (FPI), fuzzy logic controller (FLC), and model predictive control (MPC). To meet strict environmental laws, wastewater treatment plants (WWTPs) must balance efficiency and cost-effectiveness in their extremely non-linear operations. The ASM3bioP framework inside a BSM1-P is employed in this study to enable simultaneous nitrogen and phosphorus removal using an activated sludge process model with seven reactor configurations. The activated sludge process is the most complicated and energy-intensive phase of a WWTP. To control dissolved oxygen in aerobic reactors and nitrate levels in anoxic reactors, two robust PI controllers – a classical PI and a non-integer (fractional) order PI – with both integer-order and fractional-order models are designed. The controllers are created and simulated with the use of a mathematical model that has been developed based on the input data. Control theory has actively explored fractional calculus and its applications in recent years. This work regulates DO and NO concentrations in aerobic and anoxic reactors using IMC-based fractional filters cascaded with PI and FPI controls. Based on integer and non vi | P a g e integer commands, these controllers optimize plant efficiency, longevity, production costs, and effluent nutrient content. IMC fractional PI controllers prioritize maximal sensitivity (Ms) within gain margin (GM) and phase margin (PM) as limitations. Fractional-order calculus advances highlight the dynamic character of real-time complicated processes, reducing complexity while retaining complex system dynamics. The fractional-order PID (PIλDμ) controller, an improved variant of the integer-order PID, adds integration (λ) and differentiation (μ) orders, improving closed-loop response stability with parameter alterations. The lower level Fractional controller with a fractional order model improves both the effluent quality index (EQI) and operational cost index (OCI) significantly. For such biological WWTP, a hierarchical Fuzzy logic controller or a MPC are designed to adjust the dissolved oxygen in the seventh reactor (DO7) to control ammonia. The implemented supervisory layer control strategy improves EQI while increasing OCI marginally. The treatment of wastewater is highly challenging due to large fluctuations in flow rates, pollutants, and variable influent water compositions. A sequencing batch reactor (SBR), and Modified SBR Cycle-Step-Feed Process (SSBR) configuration are studied in this work to effectively treat municipal wastewater while simultaneously removing nitrogen and phosphorus. To control the amount of Dissolved Oxygen in a SBR, three axiomatic control strategy (PI, FPI), and Fuzzy logic controllers (FUZZY)) is presented. A biological process and relevant control algorithm has been designed using real-time plant data with the models of biological processes (SBR, and SSBR), and aeration system. ASM2d mathematical modelling framework is considered for development of control relevant simulations. The use of the intricate ASM2d model, as well as the application of a control strategy to a batch process, makes the work significant. A comparison of plant performance concerning PI, FPI and FUZZY control framework is included. A comparison of FPI with the other two control strategies showed a significant reduction in nutrient levels and added an improvement in effluent quality. The SSBR, which is improved by precisely optimizing nutrient supply and aeration, establishes a delicate equilibrium. This refined method reduces oxygen requirements while reliably sustaining important biological functions. This thesis also proposes a novel supervisory control scheme for SBR based WWTP. It integrates hierarchical fuzzy control, based on ammonia and nitrate observations, in the presence of lower-level PI and FPI controllers, with the dual goal of aeration cost reduction and effluent quality enhancement. In the hierarchical control system, variable DO trajectories are generated by the supervisory fuzzy logic controller and passed to the lower level controller, vii | P a g e according to ammonia and nitrate profiles within SBR. It is crucial to adjust this element properly in order to maximize wastewater treatment efficiency and reduce plant costs, especially for the aeration system. A notable aspect of nitrate based hierarchical control scheme is to curtail the fresh oxygen use since nitrate (SNO), a product of nitrification, is utilized for limiting aeration costs. Six distinct control techniques are implemented of which PI and FPI controllers for control of DO at the lower level. Four types of hierarchical ammonia and nitrate based controllers employing intelligent Fuzzy control are deployed. Addition of Fuzzy controller contributes to an airflow reduction of 40.08% for ammonia control and 31.58% for nitrate control strategies. This study highlights the superiority of the ammonia-based control strategy, particularly coupled with lower level FPI controller, based on its ability to minimize airflow without affecting effluent quality. These findings offer helpful insights for advancing the field of wastewater treatment, improving efficiency, and promoting cost-effective and sustainable practices in SBR. Another study which aims to investigate the effect of different seasons where the temperature would be different on the performance (phosphorous, nitrogen, and organic matter removal) of SBR based wastewater treatment plants. The modified ASM2d module, including the microbial kinetics is used to simulate the EBPR-based SBR process and the temperature is chosen between 10 to 33°C. Influent data from distinct wastewater treatment plants located in India and Europe are considered. The investigation of the kinetic variables is performed over a wide temperature range, and significant increases are seen as the temperature rises. The effluent parameters are within the government guidelines. It is clear that an increase in temperature results in better effluent quality with reduced COD, BOD, NH, and TN values and a slight increase in TP and TSS. In conclusion, this study highlights the importance of considering the effect of temperature on the performance of SBR-based wastewater treatment plants in different climatic conditions. Description: NITW Sun, 01 Jan 2023 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3470 2023-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3434 Title: DEVELOPMENT OF NON-NOBLE ELECTROCATALYSTS FOR QUINONE BASED ORGANIC REDOX FLOW BATTERIES Authors: KHAN, IRSHAD ULLAH Abstract: The surge in global energy generation from renewable sources has triggered the need for large scale electrical energy storage technologies. These storage technologies act like a bridge between generation and end usage. Recently, the vanadium redox flow battery (VRFB) technology has taken the lead in the market; however, the high cost of vanadium electrolyte raises concern over commercial usage. In this work, the organic flow battery systems have been proposed as an alternative to VRFB as they are simple, economical, and abundantly available. The quinone-based redox chemistry was explored and proposed two battery systems, i.e., (i) hydrogen-1,4 p-Benzoquinone redox flow battery (H2-BQ RFB) and (ii) hydroquinone benzoquinone redox flow battery (HQ-BQ RFB). Carbon-based electrodes are extensively used as electrode materials due to their comprehensive properties, but they possess poor hydrophilic nature and low electrochemical activity. It is essential to modify carbon-based materials before employing them in battery applications. Numerous techniques are proposed to refine carbon based materials. The modification by incorporating catalysts is one of the best ways to improve redox kinetics. Metal oxides are widely preferred as electrocatalysts to modify carbon due to their low cost, tunability, and high activity. The Tungsten trioxide (WO3) and Titanium dioxide (TiO2) have already proved their suitability in energy systems with exceptional electrochemical activity. Hence, these materials are proposed for the first time for the H2-BQ RFB and HQ-BQ RFB. The present work focuses on developing non-noble Tungsten trioxide-doped carbon (WO3/C) and TiO2 supported on carbon nanoparticles (TiO2@CNP) electrocatalyst for proposed organic redox flow batteries. The catalysts are characterized by field emission scanning electron microscope (FESEM) for topography, X-ray diffraction (XRD) for examining crystallinity, and Fourier transform infrared spectroscopy (FTIR) for identifying the functional bonds. TiO2@CNP and WO3/C electrocatalysts are tested in positive half-cell of H2-BQ RFB and single-cell HQ-BQ RFB, respectively. The electrochemical activity of the electrocatalysts is evaluated by cyclic voltammetry (CV). The voltammograms of TiO2@CNP-CP in positive half cell of H2-BQ RFB, TiO2@CNP-CP in both half cells of HQ-BQ RFB and WO3/C of in both half cells of HQ-BQ RFB were recorded. These show high anodic and cathodic peak currents, low charge transfer resistance, and relatively high electro-kinetic reversibility. Enhanced electrochemical active surface area (ECSA) and turnover frequency (TOF) revealed higher electrode kinetics than the pristine carbon paper. ECSA of WO3/C-CP and TiO2@CNP-CP in vi positive half-cell of HQ-BQ RFB are 10 cm2 and 8.33 cm2 respectively while in negative half cell of HQ-BQ RFB are 4.8 cm2 and 2.25 cm2. The higher ECSA implies that the WO3/C-CP provides more active sites for the reactions to take place in the battery system. The calculated number of active sites of WO3/C-CP in the positive half-cell and the negative half-cell of HQ BQ RFB are 4.3 × 10−6 mol and 2.6 × 10−6 mol, respectively. The corresponding TOF of WO3/C-CP in the positive and negative half-cells of HQ-BQ RFB are 0.19 s−1 and 0.079 s-1, respectively. These values are observed to be higher than that of CP, TiO2@CNP-CP, and possess improved catalytic activity. The TiO2@CNP electrocatalyst was tested in an H2-BQ RFB positive half-cell by galvanostatic charge-discharge and obtained energy efficiency of up to 73%. The charge–discharge test on single cell HQ-BQ RFB was conducted using pristine carbon paper (CP), TiO2@CNP-CP, and WO3/C coated carbon paper (WO3/C-CP). The columbic efficiency (CE), voltage efficiency (VE), and energy efficiency (EE) of the RFB using WO3/C-CP are around 90%, 75%, and 70%, respectively, which are significantly higher than the CP and TiO2@CNP-CP. The developed non-noble WO3/C and TiO2@CNP electrocatalysts proved their suitability in proposed H2-BQ RFB and HQ-BQ RFB systems by providing higher active sites and reaction kinetics. Description: NITW Sun, 01 Jan 2023 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3434 2023-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3433 Title: Carbon Nanofibrous Binary and Ternary Composites as High-Performance Electrodes for Supercapacitors Authors: KIRAN, DONTHULA Abstract: In response to climate concerns and the global energy crisis, the focus has shifted to renewable energy sources like solar and wind power. However, their intermittent nature creates a challenge in matching energy supply with demand. Electrochemical energy storage devices, especially supercapacitors, offer advantages over batteries but lack high energy density. Scientists are tackling this by developing electrode materials with high capacitance and optimizing electrode architecture. The goal is to enhance supercapacitors' energy density while maintaining safety and power density, paving the way for more efficient and eco-friendly energy storage solutions. This doctoral dissertation is devoted to the advancement of high-performance supercapacitors through the utilization of cutting-edge carbon-based materials, electrochemically stable metal oxides. The aim is to tackle and surpass the limitations typically associated with supercapacitors. In the present study, two dimensional MXene embedded carbon nanofiber (CNF) and pseudocapacitive material (metal oxides & conductive polymers) composites are synthesized using different techniques such as electrospinning, electrodeposition, and in-situ polymerization. Prepared electrode surface morphology, chemical composition, and microstructure analysis were performed using a range of techniques such as FESEM. HRTEM, XRD, FTIR, and BET surface area analysis. A thorough assessment of electrochemical performance was carried out using both two- and three-electrode systems using synthesized binary and ternary composites as electrode materials for the symmetric supercapacitor. The first objective of the thesis deals with the synthesis of ruthenium oxide/MXene/CNF ternary nanocomposite using a facile electrospinning method. CNF is a host material in this nanocomposite, which acts as a backbone to the ruthenium oxide (RuO2) and MXene. The electrochemical performance of the ternary composite electrode is investigated. The second aim involves synthesizing a polyaniline (PANI)/MXene/CNF core and shell ternary nanofibrous electrode. The core consists of MXene-embedded carbon nanofiber, while the shell is formed by PANI through the in-situ polymerization method. The electrochemical performance of electrodes with PANI coating for various times is investigated. The third attempt demonstrates the cobalt oxide (Co3O4) /MXene/CNF hollow nanofiber composite using core and shell electrospinning technique. Carbon nanofibers with embedded MXene and coated with cobalt oxide are used as high performance electrodes for symmetrical supercapacitors. vi The fourth objective demonstrates a facile technique to synthesize Co-based metal organic framework MOFs and MXene embedded in CNFs. The electrochemical performance of electrodes with and without MOF was investigated and compared. The fifth objective deals with the development of an artificial neural network model for the prediction of carbon nanofibrous supercapacitors' performance. We have used a data driven Artificial Neural Network (ANN) model to predict the performance of CNF electrodes based on the material microstructural properties and electrochemical operational parameters. In summary, the outcomes from various systems of MXene and CNF-based nanocomposites demonstrate efficiency across different synthesis methods. We have explored diverse pathways for synthesizing nanostructured composite materials tailored for symmetrical supercapacitor applications. Notably, the MXene-assisted synthesis proved to be energy-efficient, exhibiting high performance and robust cycling stability. The combination of carbon nanofibers, MXenes, and metal oxides yielded a synergistic effect, leading to elevated specific capacitance, improved energy density, power density, and cycling stability. Sun, 01 Jan 2023 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3433 2023-01-01T00:00:00Z