Browsing by Author "Rao, Chinta Sankar"

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Microwave Assisted Pyrolysis of Biomass and Waste Plastics: Experiments and Modeling Using Machine Learning
(National Institute of Technology Karnataka, Surathkal, 2024) Ramesh, Potnuri; Rao, Chinta Sankar
Thermochemical processes offer a promising avenue for valorizing resources from biomass and plastic waste by converting them into valuable products such as fuels, chemicals, and materials through high-temperature reactions. These methods mitigate environmental pollution by diverting waste from landfills and contribute to sustainable resource utilization and energy production. Pyrolysis is a highly efficient thermochemical technology that produces fuels and chemical intermediates. It can be carried out using conventional, solar, or microwave-controlled heating. Microwave-assisted pyrolysis offers several advantages over conventional pyrolysis methods. The distribution of temperature, rates of mass transfer, and heat transfer rates all depend on the specific operation mode and process parameters. Optimizing the pyrolysis process is essential for scaling up production. Utilizing computer-assisted modeling and simulation techniques is beneficial in developing effective configurations and experimental methods to improve efficiency. Through modeling, one can determine the optimal operating parameters and better understand the transportation mechanisms involved in pyrolysis. Machine learning (ML) is particularly advantageous when dealing with complex and nonlinear physical, chemical, and thermal processes. It also presents an opportunity to tackle data-driven challenges, extract information from large datasets, and unveil the underlying thermochemical conversion methods. ML models are constructed to accurately predict the yields of solid and liquid products obtained from the pyrolysis of biomass and plastic waste. A support vector regression model was developed to predict pyro-product yields from microwave-assisted co-pyrolysis of biomass and waste plastics. Hence, the present study aims to evaluate the microwave-assisted catalytic pyrolysis of torrefied biomass waste to produce biochar, biogas, and bio-oil. The next study investigates the microwave-assisted catalytic co-pyrolysis of torrefied sawdust (TSD) and polystyrene (PS) to obtain biochar, biogas, bio-oil, and value-added products. The design of experiments (DOE) was used to analyze the impact of torrefied sawdust and catalyst KOH loading on pyrolysis process conditions. The role of torrefaction in co-pyrolysis of (TSD: PS) was analyzed to understand the product yields, synergy, and energy consumption. The pyrolysis and co-pyrolysis product yields, average heating rate, microwave conversion efficiency, susceptor thermal efficiency, mass loss, conversion, and pyrolysis temperature will be analyzed using the ML technique. The predicted values will be compared with the experimental ones for the optimal parameters of all variables. The pyrolysis product yields were analyzed using SEM, BET, XRD, FTIR, ICP-OES, and Raman spectroscopy.
Robust Multivariable Controller Design for an Activated Sludge Process
(National Institute of Technology Karnataka, Surathkal, 2023) Anchan, Sanjith S.; Rao, Chinta Sankar
The importance of freshwater supply and safely treated wastewater return cannot be overemphasized. The human race is still a long way from the most efficient, economi- cal, and reliable ways to ensure our cities with a properly equipped treatment system. It demands the treatment of polluted/used water without discharging it to receiving water bodies. Principally, a sudden drop of dissolved oxygen concentration is observed in receiving water bodies when the organic pollutants are discharged along with the un- treated wastewater. This reduces the self-purification character of the water body, which involves the breakdown of complex organic molecules similar to biological treatment systems. The organic effluent generally contains a large quantity of suspended and set- tleable solids that will obstruct sunlight to reach the bottom surface of water bodies which then gives rise to water pollution causing eutrophication. Particularly, this can be solved by optimizing the aeration rate for better treatment, which intern reduces energy consumption and any additional chemical dosage in biological Wastewater Treatment plants (WWTP). One tool that has been successfully implemented to achieve such goals is the WWTP process model. Model development is necessary for simulating a sys- tem’s behavior, and optimizing or controlling its performance. The main motive behind controlling any WWTP is, primarily to abide by the effluent discharge standards and secondarily to maintain the operational costs as low as possible. The robust controller designs are plant-specific, but the principle and goal remain the same. Hence in this research, a mathematical model is identified using two approaches namely the system identification technique and the Process Reaction curve method for an Activated Sludge Process (ASP). By keeping this as a benchmark, the controllers are designed that aimed to control effluent dissolved oxygen or biomass concentration and substrate concentration by manipulating the aeration rate and recycle sludge flow- rate. Two types of controllers are designed to govern the ASP system: Centralized and Decentralized controllers. Each type has its respective pros and cons which are discussed in the upcoming chapters. To overcome the challenge of the grey box model for the ASP system, a data-driven approach was selected to fit a model class for the ASP unit. This technique will reduce the effort, complex tasks, and time for the process and control engineer to develop a mathematical model of the plant. Subsequently, it is then utilized to design a centralized control system for an ASP unit.