Browsing by Author "Sankar Rao, C."

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Combination of ensemble machine learning models in photocatalytic studies using nano TiO2 - Lignin based biochar
(Elsevier Ltd, 2024) K C, A.; Sankar Rao, C.; Nair, V.
Synergizing photocatalytic reactions with machine learning methods can effectively optimize and automate the remediation of pollutants. In this work, commercial Degussa TiO2 nanoparticles and lignin based biochar (LB) where used to prepare TiO2: lignin based biochar (TLB) composites using ultrasound-assisted co-precipitation method. The photocatalytic property of the TLB composites where studied by conducting the photocatalytic degradation of a Basic blue 41 (BB41) dye. The influence of calcination temperature, T:LB compositions, catalyst dosage, initial dye pH, initial dye concentration, and illumination time on photocatalytic dye degradation were experimentally studied. The degradation efficiency of 96.72 % was obtained under optimized conditions for the photocatalyst calcined at 500 °C containing a 1:1 wt percentage of TiO2 and LB. The experimental data was further used to predict the photocatalytic degradation efficiency using Gradient Tree Boosting (GTB) and Extra Trees (ET) models. The GTB model gave the highest prediction accuracy of 94 %. The permutation variable importance revealed catalyst dosage and dye concentration as the most influential parameters in the prediction of the photocatalytic dye degradation efficiency. © 2024 Elsevier Ltd
Control and dynamic optimization of middle vessel batch distillation column for the separation of ethanol/propanol/butanol mixture
(Institution of Chemical Engineers, 2021) Krishna, P.; Desikan, B.; Sankar Rao, C.
The middle vessel batch distillation column is an alternative to the regular batch distillation column. This configuration allows simultaneous separation of the light fraction (accumulated at top), heavy fraction (accumulated at bottom) and the intermediate fraction (accumulated in the middle vessel) in a single column. The objective of this article is to extensively analyse and discuss different control systems for the middle vessel batch distillation column (MVBDC) and dynamically optimize the column. Three control structures such as composition level cascade, composition temperature cascade, and temperature control system, have been tested and evaluated. Further, a dynamic optimization study has been performed on the control system providing the fastest separation. The optimizer is set to minimize the total energy consumed during the process. This resulted in a decrease in batch time from 26.8 to 25.2 h and a 13.5% decrease in overall energy usage. The presented dynamic optimization technique and control system design is useful for improving the performance of the MVBDC. © 2021 Institution of Chemical Engineers
Design of Robust PI Controller with Decoupler for a Fluid Catalytic Cracking Unit
(American Chemical Society service@acs.org, 2019) Prabhu Teja, Y.; Sankar Rao, C.
In this work, a decoupling control system is designed for the riser section of the fluid catalytic cracking unit (FCCU). The decentralized control system is implemented on FCCU to estimate the magnitude of the interactions using relative gain array (RGA). Interactions among the loops are minimized by applying the decoupling control strategy to decentralized FCCU. Relative normalized gain array and dynamic relative gain array (dRGA) are computed for the closed-loop FCCU and used to design the decouplers for the process. The advantages of the decoupling control strategy are a simple design and it does not need extensive calculations. This method gives a dynamic decoupler in the form of lead/lag modules with time delays. PI controllers can be designed efficiently for controlling the riser temperature, mass fractions of gasoline, and LPG. The decentralized controller and the decoupling control system performances are studied on the basis of the closed-loop performance of control variables, and it is found that the decoupler performs better. © © 2019 American Chemical Society.
Enhanced PID Controller for Non-Minimum Phase Second Order plus Time Delay System
(De Gruyter peter.golla@degruyter.com, 2019) Patil, P.; Sankar Rao, C.
A tuning method is developed for the stabilization of the non-minimum phase second order plus time delay systems. It is well known that the presence of positive zeros pose fundamental limitations on the achievable control performance. In the present method, the coefficients of corresponding powers of s, s2 and s3 in the numerator are equated to ?, ? and ?times those of the denominator of the closed-loop system. The method gives three simple linear equations to get the PID parameter. The optimal tuning parameters ?, ? and ?are estimated by minimizing the Integral Time weighted Absolute Error (ITAE) for servo problem using fminsearch MATLAB solver aimed at providing lower maximum sensitivity function and keeping in check with the stability. The performance under model uncertainty is also analysed considering perturbation in one model parameter at a time using Kharitonov's theorem. The closed loop performance of the proposed method is compared with the methods reported in the literature. It is observed that the proposed method successfully stabilizes and improves the performance of the uncertain system under consideration. The simulation results of three case studies show that the proposed method provides enhanced performance for the set-point tracking and disturbance rejection with improved time domain specifications. © 2019 Walter de Gruyter GmbH, Berlin/Boston.
Exploring and understanding the microwave-assisted pyrolysis of waste lignocellulose biomass using gradient boosting regression machine learning model
(Elsevier Ltd, 2024) Sinha, S.; Sankar Rao, C.; Kumar, A.; Venkata Surya, D.; Basak, T.
The production of bio-oil is a complex process influenced by various parameters. Optimizing these parameters can significantly enhance bio-oil yield, thus improving process efficiency. This study aims to develop a predictive model for bio-oil yield using the Gradient Boosting Regression (GBR) technique. It also seeks to identify the key factors affecting bio-oil yield and determine the optimal conditions for maximizing production. The GBR model was constructed using data collected from the literature. The model's performance was evaluated based on its determination coefficients for training and testing datasets. Optimization studies were conducted to identify the best conditions for bio-oil production. The GBR model demonstrated high precision, with determination coefficients of 0.983 and 0.913 for the training and testing datasets, respectively, indicating its effectiveness in predicting bio-oil yield. The optimal conditions for maximizing bio-oil yield were identified as 20 min of pyrolysis time, a temperature of 771 °C, and 524W of microwave power. The two-way PDP analysis provided valuable insights into the interactive effects of temperature with other factors, enhancing the understanding of the dynamics of the bio-oil production process. This study not only identifies the most impactful variables for bio-oil yield but also offers critical guidance for optimizing the production process. © 2024 Elsevier Ltd
Exploring machine learning applications in chemical production through valorization of biomass, plastics, and petroleum resources: A comprehensive review
(Elsevier B.V., 2024) Mafat, I.H.; Surya, D.V.; Sharma, S.K.; Sankar Rao, C.
Machine learning (ML) is a subtype of artificial intelligence that uses a computer's ability to learn from a given set of accessible data. ML is becoming prominent in almost every business, including the domain of chemical engineering, where there have been numerous researches and investigations. This article provides a detailed overview of the use of ML in the production and characterization study of biomass, polymers, and petroleum products. Categories of ML, including classification, regression, and clustering, are also investigated to get a deeper understanding of ML. From this review, it can be concluded that ML has aided in numerous domains, such as the prediction of biomass energy, the stability of crude oil based on NMR spectroscopy, the calculation of gasoline's octane number, the estimation of fuel oil's kinematic viscosity, the classification of waste plastics, and the estimation of drilling efficiency in petroleum reservoirs, among others. Apart from this, ML has also been playing a significant role in the microwave-assisted pyrolysis of biomass, polymers, and petroleum resources. ML substantially influences chemical engineering and is especially useful for enhancing system efficiency and monitoring processes that are difficult to understand manually. Although several obstacles are associated with ML, such as black box behavior, the need for a large amount of data, and the difficulty of understanding the predictions, deploying the model in the future is uncomplicated once the learning program has been trained. © 2024 Elsevier B.V.
Hospital plastic waste valorization through microwave-assisted Pyrolysis: Experimental and modeling studies via machine learning
(Elsevier Ltd, 2025) Ramesh, R.; Sankar Rao, C.; Surya, D.V.; Kumar, A.
The COVID-19 pandemic generated a global upsurge in hospital plastic waste (HPW) as a consequence of the widespread utilization of personal protective equipment (PPE) composed of diverse polymer materials. The constant demand for PPE worldwide led to the accumulation of substantial volumes of high-polymer-based plastic waste. To tackle this challenge, researchers delved into the conversion of HPW into valuable chemicals through a process known as microwave-assisted pyrolysis (MAP). This method entails the transformation of HPW into high-quality char and liquid oil, which can serve as a source of fuel. In this study, our primary focus was to understand how the ratio of HPW (hospital plastic waste) to susceptor weight influenced the yields and characteristics of the resulting products in the context of the MAP process. To facilitate the experimental setup, a Central Composite Design (CCD) was employed. The impact of varying HPW weights and susceptor quantities on the production of value-added products was investigated. The analysis of condensed organic vapor decomposition revealed an increase in liquid yields (73.6 wt %, 76.6 wt %, 80.7 wt %) as the graphite content increased at a constant 30 g HPW. Conversely, gas yield decreased with higher susceptor and HPW quantity. Keeping the graphite constant at 4g, the gas yield declined (32.5 wt %, 30.7 wt %, and 24.7 wt %) as HPW increased. Additionally, gas yield exhibited a drop (32.5 wt % to 18.1 wt %) with an increase in both graphite and HPW. Furthermore, the residual yield decreased (from 1.7 wt % to 1.2 wt %) with a 30 g increase in HPW. In-depth analysis incorporated machine learning techniques to understand the behavior of response variables about susceptor and HPW quantities. The optimization of the MAP process for HPW encompassed various supplementary operational parameters, including susceptor thermal energy, average heating rate, microwave energy, specific microwave power, and product yields. Moreover, the residue generated from the MAP of HPW underwent characterization through X-ray diffraction (XRD), FTIR, and BET analysis. © 2025 Elsevier Ltd
Identification and Control of an Unstable SOPTD system with positive zero
(Elsevier B.V., 2018) Ram, V.; Sankar Rao, C.
The work deals with the identification and control of unstable Second Order plus Time Delay (SOPTD) system with positive zero. Presence of positive zero complicates the performance of the control system dynamics. There are many unstable systems which exhibit the second order plus time delay with positive zero such as drum boiler, distillation column. No work has been reported in the literature on identification of unstable SOPTD process with positive zero. In this work, a subspace based method and an optimization method are proposed to identify an unstable SOPTD model with positive zero followed by the PID controller design which can handle set-point changes and disturbance rejection. The subspace-based method uses input-output measurements to estimate the state space model. This method uses projections of block Hankel matrices followed by a singular value decomposition to determine the order of the system. It offers the key advantages on providing low parameter sensitivity with respect to perturbations for higher order systems. The model parameters are also identified using optimization technique by matching the closed loop responses of the process and the model. In any optimization technique, the initial guess plays an important role for proper convergence. A method is suggested to obtain the initial guess values for process gain, poles, zeros and delay. The parameters identified by subspace based method are compared with that obtained using optimization technique. For the models identified by the above two methods, controllers are designed and implemented. Simulation studies on linear and nonlinear systems are demonstrated to evaluate the performance of the proposed methodologies. The closed loop performances comparison can be made in terms of time integral errors and total variation in input variable. © 2018 Elsevier B.V.
Isoconversional Kinetic Analysis and ANN-Based Prediction of Metformin Pyrolysis for Sustainable Waste Management
(American Chemical Society, 2025) Potnuri, R.; Lenka, M.; Sankar Rao, C.; Harshini, H.
Pharmaceutical waste poses a growing environmental concern due to its persistence and potential ecological impacts, necessitating effective and sustainable management strategies. This study investigates the pyrolysis of metformin as a means to valorize pharmaceutical waste within a circular economy framework. Pyrolysis experiments conducted on 500 mg of metformin demonstrated the formation of liquid-phase products, characterized by GC–MS, which revealed a high concentration of the active pharmaceutical ingredient (API) alongside carbonaceous, nitro, and acidic compounds. Comprehensive thermogravimetric analyses at heating rates of 10, 20, 30, and 40 °C/min were performed to evaluate the thermal decomposition behavior. Kinetic parameters were determined using four isoconversional methods, namely KAS, FWO, Starink, and FRD, yielding average activation energies of 101.4, 105.8, 101.4, and 111.1 kJ/mol, respectively. Thermodynamic parameters (?G, ?H, and ?S) were also calculated to gain further insights into the decomposition process. Additionally, an ANN model was developed using temperature and heating rate as inputs to predict mass loss, achieving accurate estimations with an optimized architecture comprising two hidden layers. GC–MS analysis of the pyrolysis liquid identified a high concentration of the API, along with carbonaceous, nitro, and acidic compounds. These findings highlight the potential for API recovery and reuse, as well as the valorization of byproducts for energy or chemical synthesis. The potential recovery of APIs for reuse and the utilization of byproducts as fuels or chemical precursors underscore pyrolysis as a promising route for sustainable pharmaceutical waste management and circular economy integration. © 2025 The Authors. Published by American Chemical Society
Kinetic analysis and machine learning insights in the production of biochar from Artocarpus heterophyllus (jackfruit) through pyrolysis
(Elsevier Ltd, 2025) Tiwari, A.; Sankar Rao, C.; Jammula, K.; Balasubramanian, P.; Chinthala, M.
According to International Energy Agency (IEA) Task 40, biomass contributes approximately 10 % of global energy production. This includes waste from agriculture and forestry, generating around 140 billion tons of biomass each year—posing a major challenge for efficient management and disposal. The Food and Agriculture Organization (FAO) reports that global jackfruit production reached 3.7 million tons between 2015 and 2017, while 2.96 million tons of bioenergy feedstock were produced in 2018. Utilizing jackfruit waste as a renewable bioenergy source not only adds economic value to agricultural residues but also helps reduce overall waste generation. The bark of the jackfruit tree (Artocarpus heterophyllus (AHB)) possesses considerable economic importance and exhibits an enormous distribution throughout several regions in Asia. This study involves the production of biochar from AHB biomass through fast pyrolysis at temperatures between 400 and 600 °C. The biochar produced has a carbon content of 66.69 wt% and a calorific value of 27.15 MJ/kg, respectively, which have similar properties to coal. The kinetic analysis of biomass employed three distinct models (OFW, KAS, and TANG) to determine the activation energy. The current study employed machine learning (ML) models to forecast the mass loss of biomass during pyrolysis, which is challenging because of the intricate characteristics of biomass and the extensive range of operating circumstances. Temperature and heating rate were used as input data, while mass loss was the desired output, to train a variety of machine learning models, including ensemble learning, support vector regression, Gaussian process regression, and neural network models. Among these models, the Gaussian process regression model showed superior performance compared to others, achieving a perfect R2 of 1 and minimal errors on both the validation and test sets, making it the best model to predict mass loss of biomass. © 2025 Elsevier Ltd
Leveraging explainable AI framework for predictive modeling of products of microwave pyrolysis of lignocellulosic biomass using machine learning
(Elsevier B.V., 2025) Kale, R.D.; Lenka, M.; Sankar Rao, C.
The accurate prediction of biochar, bio-oil, and biogas yields in biomass pyrolysis is critical for optimizing process efficiency and sustainable biofuel production. In this study, machine learning (ML) models were developed using literature-derived data on biomass composition and pyrolysis conditions to predict product yields. A comparative analysis of multiple ML algorithms revealed that Decision Tree and Extra Trees exhibited the highest predictive accuracy, followed by Random Forest, Gradient Boosting Trees, and Extreme Gradient Boosting. LightGBM, Gaussian Process Regression, and CatBoost provided moderate performance, while AdaBoost demonstrated the lowest accuracy. To enhance interpretability, Explainable Artificial Intelligence (XAI) techniques, specifically SHAP analysis, were employed to identify key factors influencing pyrolysis yields. Temperature, ash content, fixed carbon, and moisture content were the dominant parameters governing biochar yield, whereas heating rate, reaction time, and feedstock properties such as carbon and volatile matter content significantly influenced bio-oil production. The gas yield was primarily driven by temperature, with secondary cracking mechanisms enhancing non-condensable gas formation. These insights provide a data-driven foundation for optimizing biomass pyrolysis processes, enabling targeted valorization strategies for lignocellulosic feedstocks. The integration of ML and XAI in this study establishes a transparent and interpretable modeling framework, facilitating informed decision-making for sustainable biofuel production. © 2025 Elsevier B.V.
Microwave assisted catalytic co-pyrolysis of banana peels and polypropylene: experimentation and machine learning optimization
(Royal Society of Chemistry, 2025) Rajpurohit, N.S.; Sinha, S.; Ramesh, R.; Sankar Rao, C.; Harshini, H.
The growing accumulation of agricultural and plastic waste poses serious environmental challenges, necessitating sustainable and efficient valorization strategies. This study investigates the microwave-assisted catalytic co-pyrolysis of banana peels and polypropylene, using graphite as a susceptor and potassium hydroxide as a catalyst. Experiments were conducted by varying biomass and plastic quantities and microwave power levels to study their effects on product yields and thermal performance. The process effectively converted waste materials into valuable products, with oil yield increasing with microwave power and optimized biomass-to-plastic ratios. The rate of mass loss and heating rate were found to significantly influence overall conversion efficiency. A support vector regression (SVR) model was developed to predict yields based on input parameters, achieving a coefficient of determination ranging from 0.81 to 0.99, which demonstrates the reliability of machine learning in capturing complex thermochemical behavior. 3D plots illustrated the nonlinear effects of process variables on yields. Fourier Transform Infrared Spectroscopy (FTIR) and X-ray Diffraction (XRD) analyses of char confirmed functional groups and crystalline phases, suggesting its suitability for applications like adsorbents or catalysts. Brunauer-Emmett-Teller (BET) analysis showed multilayer adsorption, while thermogravimetric analysis (TGA) highlighted distinct thermal degradation patterns of the feedstocks. These results affirm the promise of integrating experiments with ML for efficient waste-to-energy conversion. © 2025 The Royal Society of Chemistry.
Microwave-assisted torrefaction of lignocellulosic biomass: A critical review of its role in sustainable energy
(Elsevier Ltd, 2025) Ramesh, R.; Sankar Rao, C.; Lenka, M.; Sridevi, V.; Basak, T.
Lignocellulosic biomass is a promising renewable energy source that can help reduce reliance on fossil fuels. However, its raw form presents challenges for practical use. To overcome this, the Microwave-assisted torrefaction (MAT) process has emerged as a successful method for enhancing the quality of biomass and generating energy. This article aims to provide a comprehensive review of recent scientific research on MAT of biomass. It explores torrefaction indices and discusses the impact of key parameters such as biomass composition, temperature, residence time, heating rate, particle size, and microwave power on MAT. The article also addresses potential applications and challenges associated with MAT. Furthermore, it evaluates the hurdles in achieving compatibility, acceptability, and sustainability of the process, along with future directions to realize economic benefits even in small-scale applications. Ultimately, MAT holds promise as an energy-efficient approach to enhance the effectiveness of biomass utilization. © 2025
Predicting synergistic effects on biofuel production from microalgae (Spirulina)/Tire Co-pyrolysis using ensemble machine learning
(Elsevier B.V., 2025) Sridevi, V.; Al-Asadi, M.; Adnan Abdullah, T.; Nhat, T.; Sankar Rao, C.; Talib Hamzah, H.; Le, P.-C.
This study investigates the synergistic effects of microwave-assisted catalytic co-pyrolysis (MACCP) of microalgae and waste tires (WT) under varying parameters such as catalyst weight, microwave power, and susceptor quantity. Optimal reaction conditions yielded a high-quality bio-oil with a maximum yield of 50.46 wt% with low water content, significantly reducing microwave energy consumption from 810 to 540 kJ. The co-pyrolysis of WT and microalgae enhanced denitrogenation and deoxygenation, improving the quality of the resulting bio-oil. Gas chromatography-mass spectrometry (GC-MS) analysis of bio-oil identified an increase in the complex composition of mono- and polyaromatic hydrocarbons and a decrease in oxygenated compounds. An ensemble machine learning approach has been employed to model and predict outcomes, achieving R2 values between 0.7 and 0.98. The models with the best predicted accuracy were Extreme Gradient Boosting (XGB) and Extra Trees (ET), both of which achieved an R2 of 0.98. The models were rigorously validated using the Leave-One-Out Cross-Validation technique, ensuring robust predictions with minimal bias by training on all but one observation iteratively and testing on the excluded data point. The work highlights the possible use of co-pyrolyzing microalgae and WT for sustainable, high-quality bio-oil production with lower energy consumption. It shows that machine learning can optimize MACCP procedures. © 2025 The Energy Institute
Predictive modeling of product yields in microwave-assisted co-pyrolysis of biomass and plastic with enhanced interpretability using explainable AI approaches
(Elsevier B.V., 2025) Rajpurohit, N.S.; Kamani, P.K.; Lenka, M.; Sankar Rao, C.
Microwave-assisted co-pyrolysis of biomass and plastic offers a transformative approach to converting waste into valuable resources such as bio-oil, biochar, and biogas, while simultaneously addressing critical environmental challenges associated with plastic disposal. This research employs explainable AI methodologies to enhance the prediction and analysis of product yields in biomass-plastic co-pyrolysis. Advanced machine learning techniques, including Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks, were utilized to model yield predictions effectively. The models were fine-tuned through hyper-parameter optimization, achieving high accuracy levels. The study emphasizes the scientific importance of integrating explainable AI with pyrolysis processes to optimize waste-to-resource recovery, contributing significantly to sustainable waste management and circular economy initiatives. Among these, the XGBoost model demonstrated superior performance, achieving R² values of 0.91 for biochar yield, 0.92 for bio-oil yield, and 0.82 for biogas yield on testing sets. To enhance model interpretability, SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) were utilized to assess feature importance and examine parameter influences on yield outcomes, offering valuable insights into process optimization and control. Volatile matter and fixed carbon were key predictors for biochar yield, while moisture content and pyrolysis temperature were significant for predicting bio-oil and biogas yields. This study highlights the potential of explainable AI models in advancing sustainable and efficient bio-product recovery from waste materials. © 2025 Elsevier B.V.
Robust optimal centralized PI controller for a fluid catalytic cracking unit
(De Gruyter Open Ltd, 2021) Yadav, G.; Kiran, G.U.; Sankar Rao, C.
Fluidized Catalytic Cracking (FCC) is a complex process that arises due to feed composition, non-linearities, and dynamic mass and heat interactions in its components. FCC is difficult to model and monitor in industries, and one of the key reasons is that they are multivariable processes. Such processes are highly interacting and that makes the process of controlling even more difficult. The interaction between loops can be quantified easily by dRGA. An easy and effective way of controlling multivariable processes is to implement a centralized control system, considering the interactions between measured and manipulated variables. In this study, a centralized control system is designed for the riser section of the FCC unit. The dRGA method is modified to enhance the closed-loop response by formulating an optimization problem and obtaining an optimal controller settings. A rigorous simulation studies show an 826% reduction in ISE values, a 309% reduction in IAE values, and a 262% reduction in ITAE value of T r i s ${T}_{ris}$ from the dRGA method to the modified dRGA method. Further, IAE values for Y l p g are reduced by 29% from dRGA to modified dRGA method and 34% from synthesis to modified dRGA method. © 2020 Walter de Gruyter GmbH, Berlin/Boston.
Role of ZSM5 catalyst and char susceptor on the synthesis of chemicals and hydrocarbons from microwave-assisted in-situ catalytic co-pyrolysis of algae and plastic wastes
(Elsevier Ltd, 2022) Suriapparao, D.V.; Tanneru, T.; Rajasekhar Reddy, B.R.; Yerrayya, A.; Bhasuru, B.A.; Pandian, P.; Prakash, S.R.; Sankar Rao, C.; Sridevi, V.; Desinghu, J.
The synergetic effect between algae biomass in co-pyrolysis with synthetic plastics (polypropylene (PP), polyethylene (PE), and expanded polystyrene (EPS)) was investigated in this work. Individual feedstock pyrolysis and co-pyrolysis of algae with PP, PE, and EPS were conducted at a constant supply of microwave energy (420 J/s). Pyrolysis char was used as a susceptor in all the experiments. The average heating rate was varied in the range of ∼50–60 °C/min for achieving the final pyrolysis temperature of 600 °C. In catalytic co-pyrolysis, the ZSM-5 catalyst was used for upgrading the physicochemical properties of pyrolysis oil. The use of catalyst promoted the excessive cracking of biomass in co-pyrolysis, leading to higher gas and coke residue comparatively. The viscosity, density, and flash point of oil obtained in catalytic co-pyrolysis were significantly reduced. While the oil obtained from individual pyrolysis of algae is rich in phenolic derivatives, and that of PP, PE has aliphatic hydrocarbons, and EPS has monoaromatic hydrocarbons as major compounds. The synergistic role of plastic and biomass in co-pyrolysis was observed in the formation of products and oil composition. The bio-oil from catalytic co-pyrolysis is composed of aliphatic oxygenates, aliphatic hydrocarbons, cyclic aliphatic hydrocarbons, and phenolics. The chemicals and hydrocarbons present in the oil have a carbon number in the range of C6 to C30. An increase in carbon and hydrogen elemental composition was observed in bio-oil obtained from co-pyrolysis. © 2021 Elsevier Ltd
Synergistic effects and product yields in microwave-assisted in-situ co-pyrolysis of rice straw and paraffin wax
(Institution of Chemical Engineers, 2024) Hamzah, H.T.; Sridevi, V.; Surya, D.V.; Ramesh, P.; Sankar Rao, C.; Palla, S.; Abdullah, T.A.
Microwave-assisted pyrolysis is one of the most efficient methods for solid waste management. This study employed microwave-assisted catalytic co-pyrolysis to convert Paraffin wax (PW) and rice straw (RS) into valuable char, gas, and oil products. KOH and graphite were used as the catalyst and susceptor, respectively. The RS and PW blend served as the feedstock (with a blend ratio of 0–10 g). The yields of co-pyrolysis at different blending ratios of RS: PW exhibited variations in char content (ranging from 9.8% to 22.6% by wt.), oil production (ranging from 34.1% to 76.9% by wt.), and gas formation (ranging from 13.2% to 47.5% by wt.). The effects of the RS: PW ratio on the average heating rate, feedstock conversion, and product yields were also investigated. Analyses were performed to assess the synergistic impacts on product yields, average heating rates, and conversion factors. Notably, co-pyrolysis synergy led to increased oil and char production. Furthermore, we conducted FTIR analysis on the oil and char produced through the catalytic co-pyrolysis of RS: PW. In conjunction with co-pyrolysis synergy, the catalyst facilitated the formation of amides, alkenes, aliphatic compounds, and aromatic compounds. © 2023 The Institution of Chemical Engineers
Synthesis and Characterization of Biochar Obtained from Microwave-Assisted Copyrolysis of Torrefied Sawdust and Polystyrene
(American Chemical Society, 2024) Potnuri, R.; Sankar Rao, C.
This study focuses on copyrolyzing pretreated sawdust and polystyrene utilizing microwave-assisted pyrolysis (MAP) with equal mixing to synthesize and characterize biochar. Graphite was used as a susceptor to facilitate precise pyrolysis temperature control. Potassium hydroxide (KOH) powder serves as a catalyst, influencing the char yields and properties. Torrefied raw sawdust at various temperatures (125–175 °C) enhances biochar yields (24–29 wt %). The feedstocks sawdust and polystyrene are characterized by elemental, proximate, and TGA examinations. Furthermore, comprehensive surface, crystallographic, FTIR, and SEM-EDX analyses are performed on microwave copyrolyzed biochar. The developments in BET surface area during copyrolysis show changes concerning pretreatment temperatures: 125 °C (5.6 m2/g) < 150 °C (6.8 m2/g) < 175 °C (8.6 m2/g). Functional groups connected to the alcohols’ O–H bend and C–O stretching vibrations are detected in the biochar samples through FTIR analysis. Sharp peaks with 2θ values between 33.2° and 36.2° appear in the XRD scan of biochar, indicating the presence of crystalline components in the sample. The EDX results demonstrated that the components of biochar included Mg, C, O, and Ca, indicating that it could have plenty of advantageous applications. The study highlights the obstruction of sawdust char’s porous structures by polystyrene, hindering volatile emissions and leading to increased heating rates. These findings underscore the unique contributions of this method to biochar production. © 2024 American Chemical Society
Tuning optimal PID controllers for open loop unstable first order plus time delay systems by minimizing ITAE criterion
(Elsevier B.V., 2020) Sankar Rao, C.; Santosh, S.; Ram, V.
The present paper gives an optimal tuning procedure for design of Proportional Integral Derivative (PID) controllers for open loop unstable First Order plus Time Delay systems. Estimation of the optimal PID controller parameters: kc, tI and tD is carried out by minimizing the time integral performance criteria:ITAE(Integral of Time-weighted Absolute Error) for a setpoint tracking problem. Results of the simulation for linear transfer functions and nonlinear isothermal CSTR reveals the proposed tuning approach provides enhanced performance for the set-point tracking and disturbance rejection with improved time domain specifications. To evaluate the superiority of the proposed method over others, robustness analysis with complimentary sensitivity function is carried out. Â© 2020, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.