Browsing by Author "Sankar Rao, C.S."

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A review on analysis of biochar produced from microwave-assisted pyrolysis of agricultural waste biomass
(Elsevier B.V., 2023) Ramesh, R.; Surya, D.V.; Sankar Rao, C.S.; Yadav, A.; Sridevi, V.; Remya, N.
Every year the agricultural product processing industries produce large quantities of agricultural waste biomass (AWB). Whose disposal has become a serious issue concerning solid waste management due to environmental and health issues. Microwave-assisted pyrolysis (MAP) is an intriguing technology for producing valuable products from waste feedstocks. AWB is converted into a valuable product like biochar by using MAP. The conversion of AWB into biochar by MAP is influenced by several factors such as type of feedstock, pyrolysis temperature, residence time, pressure, heating rate, susceptor, particle size, and microwave power. However, no review article is available to understand the role of MAP on biochar production from AWB. The current review focused on understanding the fundamentals of biochar production. It also reviews the challenges in producing biochar process by compatible, acceptable, and sustainable and its future directions to gain economic benefits even at small-scale applications. The generation of biochar from MAP and its uses in agriculture are discussed. The current review would address the knowledge gap and highlight the critical implications in biochar production and applications. © 2023 Elsevier B.V.
A review on the role of various machine learning algorithms in microwave-assisted pyrolysis of lignocellulosic biomass waste
(Academic Press, 2024) Mafat, I.H.; Surya, D.V.; Sankar Rao, C.S.; Kandya, A.; Basak, T.
The fourth industrial revolution will heavily rely on machine learning (ML). The rationale is that these strategies make various business operations in many sectors easier. ML modeling is the discovery of hidden patterns between multiple process parameters and accurately predicting the test values. ML has provided a wide range of applications in Chemical Engineering. One major application of ML can be found in the microwave-assisted pyrolysis (MAP) of lignocellulose bio-waste. MAP is an energy-efficient technology to obtain high-saturated hydrogen-rich liquid fuels. The main focus of this review study is understanding the utilization of various types of ML algorithms, including supervised and unsupervised techniques in microwave-assisted heating techniques for diverse biomass feedstocks, including waste materials like used tea powder, wood blocks, kraft lignin, and others. In addition to developing effective ML-based models, alternative traditional modeling approaches are also explored. In addition to various thermochemical conversion processes for biomass, MAP is also briefly reviewed with several case studies from the literature. The conventional modeling methodology for biomass pyrolysis with microwave heating is also discussed for comparison with ML-based modeling methodologies. © 2024 Elsevier Ltd
A Simple Method to Design a Decoupler for a Proton Exchange Membrane Fuel Cell
(John Wiley and Sons Inc, 2022) Goyal, I.; Reddy, S.; Sankar Rao, C.S.
A decoupling control system is designed by a simple tuning-free method for the multi-input multi-output model of a proton exchange membrane fuel cell. The decoupler minimizes the interactions among the loops and is designed by estimating the relative normalized gain array and dynamic relative gain array for the closed-loop model. The proportional integral controller settings are estimated using the partial model matching method. The performances of the proposed method are studied based on closed-loop performances of control variables and time integral errors that are compared with the synthesis method. The proposed controller performs better in terms of integral of time-weighted absolute error. © 2022 Wiley-VCH GmbH.
Centralized Proportional Integral Controller Design for the Activated Sludge Process
(John Wiley and Sons Inc, 2022) Anchan, S.S.; Sankar Rao, C.S.
The design of a centralized proportional integral controller for a single-tank activated sludge process is presented. The relative normalized gain array (RNGA) and dynamic relative gain array (dRGA) were adopted for enhancing the closed-loop performance of a multivariable system. The dRGA method gave superior and acceptable responses compared to the RNGA method. The designated error indices decreased significantly for the dRGA method compared with the RNGA method. The dRGA method is simple and effective, since it considers the process dynamics to provide an accurate interaction assessment. The designed centralized controller overcomes the limitations of the decoupler for stability challenges when compared with decentralized controllers. © 2022 Wiley-VCH GmbH.
Development of machine learning model for the prediction of selectivity to light olefins from catalytic cracking of hydrocarbons
(Elsevier Ltd, 2025) Mafat, I.H.; Sharma, S.K.; Surya, D.V.; Sankar Rao, C.S.; Maity, U.; Barupal, A.; Jasra, R.
Light olefins are the primary building block for the production of petrochemicals and polymers. Light olefins are largely produced from steam/catalytic cracking of naphtha or ethane/propane. Selectivity to light olefins is significantly dependent on the reaction conditions. In this article, several machine learning models are developed and tested to predict the selectivity of ethylene and propylene using seven input features. For this study, a total of eight ML models consisting of adaptive boost, extreme gradient boost, categorical boost, light gradient boost, decision tree with bagging, random forest, k-nearest neighbour, and artificial neural models are developed. The extreme gradient boost model gave the highest prediction accuracy for the ethylene selectivity, while the light gradient boost gave the highest R2 for the propylene selectivity. The SHAP analysis showed the input parameter's importance ranking for ethylene predictions as temperature > number of carbon atoms > Si/Al ratio > acidity > weight hourly space velocity > effect of diluent > number of hydrogen atoms. The importance ranking of input parameters for propylene selectivity was observed as weight hourly space velocity > acidity > temperature > Si/Al ratio > effect of diluent > number of carbon atoms > number of hydrogen atoms. © 2024 Elsevier Ltd
Dynamic performance comparison of two configurations of middle vessel batch distillation column for the separation of ethanol/propanol/butanol mixture
(John Wiley and Sons Ltd cs-journals@wiley.co.uk, 2020) Narayana, M.S.; Arthanareeswaran, G.; Sankar Rao, C.S.
This paper deals with Aspen Plus and Aspen Dynamics of the middle vessel batch distillation for the separation of mixtures of ethanol/propanol/butanol. Two configurations of middle vessel batch distillation have been considered, namely, the conventional middle vessel batch distillation (Configuration 1) and the modified middle vessel batch distillation column (Configuration 2). Steady-state simulations have been performed in Aspen Plus and exported to Aspen Dynamics for dynamic simulation. Dynamic studies show that Configuration 1 requires less time than Configuration 2 to obtain more than 95% of the compositions of ethanol, propanol, and butanol. The efficacy of the two controllers is assessed by the performance indices of integral of square error, integral of absolute error, and integral of time-weighted absolute error. Configuration 1 is found to have better performance than Configuration 2. © 2020 Curtin University and John Wiley & Sons, Ltd.
Effect of dry torrefaction pretreatment of the microwave-assisted catalytic pyrolysis of biomass using the machine learning approach
(Elsevier Ltd, 2022) Ramesh, R.; Suriapparao, D.V.; Sankar Rao, C.S.; Sridevi, V.; Kumar, A.
This study employs the Leave-One-Out cross-validation approach to build a machine-learning model using polynomial regression to predict pyro product yield through microwave-assisted pyrolysis of sawdust over KOH catalyst and graphite powder a susceptor. The determination of coefficient (R2) validates the developed models. All the developed models achieved a high prediction accuracy with R2 > 0.93, which signifies that the experimental values are in good agreement with the predicted one. The dependence of the catalyst loading and pretreatment temperature on dominating process parameters such as heating rate, pyrolysis temperature, susceptor thermal energy, and pyro products, namely bio-oil, biochar, and biogas, are explored. The yield of biochar is reduced; however, bio-oil and biogas are enhanced as the catalyst loading increased. On the other hand, increasing the temperature of pretreated sawdust decreased bio-oil and biogas yields while increasing biochar yields. Further, microwave conversion efficiency, and susceptor thermal energy increased with increased catalyst quantity and pretreatment temperatures of sawdust. It was observed that the average heating rate was increased by increasing the catalyst quantity while maintaining the same pyrolysis time until pretreatment temperatures of 150 °C were reached, after which the heating rate dropped due to the continuous microwave energy input to the system. © 2022 Elsevier Ltd
Effective electronic waste valorization via microwave-assisted pyrolysis: investigation of graphite susceptor and feedstock quantity on pyrolysis using experimental and polynomial regression techniques
(Springer, 2024) Mistry, C.; Surya, D.V.; Ramesh, R.; Basak, T.; Kumar, P.S.; Sankar Rao, C.S.; Gautam, R.; Sridhar, P.; Choksi, H.; Remya, N.
Waste printed circuit board (WPCB) was subjected to microwave-assisted pyrolysis (MAP) to investigate the energy and pyrolysis products. In MAP, pyrolysis experiments were conducted, and the effects of WPCB to graphite mass ratio on three-phase product yields and their compositions were analyzed. In addition, the role of the initial WPCB mass (10, 55, and 100 g) and susceptor loading (2, 22, and 38 g) on the quality of product yield was also evaluated. By using design of experiments, the effects of graphite susceptor addition and WPCB feedstock quantity was investigated. A significant liquid yield of 38.2 wt.% was achieved at 38 g of graphite and 100 g of WPCB. Several other operating parameters, including average heating rate, pyrolysis time, microwave energy consumption, specific microwave power used, and product yields, were optimized for the MAP of WPCB. Pyrolysis index (PI) was calculated at the blending of fixed quantity WPCB (100 g) and various graphite quantities in the following order: 2 g (21) > 20 g (20.4) > 38 g (19.5). The PI improved by increasing the WPCB quantity (10, 55, and 100 g) with a fixed quantity of graphite. This work proposes the product formation and new reaction pathways of the condensable compounds. GC–MS of the liquid fraction from the MAP of WPCBs without susceptor resulted in the generation of phenolic with 46.1% relative composition. The addition of graphite susceptor aided in the formation of phenolic and the relative composition of phenolics was found to be 83.6%. The area percent of phenol increased from 42.8% (without susceptor) to 78.6% (with susceptor). Without a susceptor, cyclopentadiene derivative was observed in a very high composition (~ 31 area %). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.
Experimental analysis, modelling, and optimisation of alkaline leaching in coal fly ash treatment
(Taylor and Francis Ltd., 2025) Murmu, A.K.; Sankar Rao, C.S.; Parida, L.; Senapati, P.K.
The present study introduces a novel integration of Gaussian Process Regression (GPR) modelling and Particle Swarm Optimisation (PSO) to improve the efficiency of alkaline leaching of coal fly ash (CFA). The selected operating variables for the alkali leaching process include temperature, leaching time, concentration of the alkalis (NaOH and KOH), and the liquid-to-solid ratio. A GPR model was employed for data fitting of the leaching process, yielding high predictive accuracy with R2 values of 0.9978 for SiO2 dissolution, 0.9742 for Al2O3 dissolution, and 0.9945 for Al/Si ratio in the NaOH-treated CFA process. In the KOH-treated CFA process, the GPR model achieved R2 values of 0.9645 for SiO2 dissolution, 0.9873 for Al2O3 dissolution, and 0.9960 for Al/Si ratio. Under optimised conditions, both NaOH- and KOH-treated leaching processes demonstrated an effective desilication of CFA, with NaOH showing higher silica dissolution and KOH yielding greater alumina recovery. The resulting Al/Si ratios further confirmed the efficiency of treatment, with the higher ratio in the NaOH process reflecting more effective silica removal. These findings demonstrate the efficacy of using PSO in conjunction with GPR models to optimise leaching processes, offering a significant advancement in the efficient processing of CFA through precise control of operational parameters. © 2025 Canadian Institute of Mining, Metallurgy and Petroleum.
Improved PID controller design for an unstable second order plus time delay non-minimum phase systems
(Elsevier B.V., 2022) Patil, P.; Anchan, S.S.; Sankar Rao, C.S.
Principally the fundamental limitations for achieving better control performance is caused due to the existence of positive zeros. This study proposes a method to stabilize the unstable non-minimum phase Second Order Plus Time Delay (SOPTD) process. It adopts the Taylor series expansion to produce the equal order of numerator and denominator for the closed loop transfer function. The coefficient of corresponding powers of s, s2 and s3 in numerators are equated to α, β, γ times of the denominator and solved for PID controller setting using multi-objective optimization problems. The stability of the controller is then analysed by minimizing the Integral Time weighted Absolute Error (ITAE) and maximum sensitivity function using MATLAB solvers. The observations from various simulation studies clearly suggests that the proposed method provides significant superior responses when compared with the methods reported. © 2022 The Author(s)
Microwave-assisted In-situ catalytic co-pyrolysis of polypropylene and polystyrene mixtures: Response surface methodology analysis using machine learning
(Elsevier B.V., 2023) Kamireddi, D.; Terapalli, A.; Sridevi, V.; Tukaram Bai, M.T.; Surya, D.V.; Sankar Rao, C.S.; Jeeru, L.R.
Polypropylene (PP) and Polystyrene (PS) are the major plastic fractions found in mixed plastic waste. Hence, the current study was focused to convert PP and PS into useful products via microwave-assisted pyrolysis (MAP). In addition, the understanding of feedstock conversion, product yields, and energy requirements in pyrolysis, co-pyrolysis, and catalytic co-pyrolysis was investigated. Experiments were conducted at a constant microwave power of 450 W till the reaction temperature reached up to 600 °C. When PS pyrolyzed, a heating rate of 56 °C/min resulted in 80 wt% of oil yield. Whereas PP pyrolysis produced 42 wt% of oil at a heating rate of 76 °C/min. In the PP: PS co-pyrolysis, the heating rate was decreased to 52 °C/min by yielding 51 wt% of oil. In catalytic co-pyrolysis of PP: PS with KOH resulted in variation in product yields and heating rate. An increase in PS quantity at a constant mass of PP resulted in the enhancement of oil yields from 58 to 84 wt% and a decrease in gas yields. The specific microwave power in the catalytic co-pyrolysis (7–18 W/g) is lower compared to the non-catalytic case (22–30 W/g). Whereas, the pyrolysis time in non-catalytic pyrolysis (7–11 min) is lower compared to catalytic co-pyrolysis (14–37 min). The addition of a catalyst resulted in a decrease (23–50%) in microwave conversion efficiency than that of the non-catalytic case (60–85%). The difference in predicted and actual result analysis proved co-pyrolysis synergy in product formation and energy consumption. © 2023 Elsevier B.V.
Microwave-assisted in-situ catalytic pyrolysis of polystyrene: Analysis of product formation and energy consumption using machine learning approach
(Institution of Chemical Engineers, 2022) Terapalli, A.; Kamireddi, D.; Sridevi, V.; Tukarambai, M.; Suriapparao, D.V.; Sankar Rao, C.S.; Gautam, R.; Modi, P.R.
Microwave-assisted catalytic pyrolysis is a prominent technology for the production of high-quality fuel intermediates and value-added chemicals from polystyrene waste. The objectives of this study were to understand the role of catalyst (KOH) on polystyrene (PS) pyrolysis. Pyrolysis experiments were conducted using a microwave oven at a power of 450 W and a temperature of 600 °C. Graphite susceptor (10 g) was used to achieve the required pyrolysis conditions. In addition, the design of experiments (DoE) with machine learning (ML) was used to understand the loading of PS (5 g, 27.5 g, and 50 g), and KOH (5 g, 7.5 g, and 10 g). The products including oil, gas, and char were collected in every experiment. The average heating rates achieved were in the range of 30–50 °C/min. The specific microwave power (microwave power per unit mass of feedstock) decreased with an increase in PS amount from 90 to 9 W/g. However, the specific microwave energy (microwave energy per unit mass of feedstock) (27–73 kJ/g) was in line with the average heating rate. The maximum yield of pyrolysis oil was found to be 95 wt%, which was obtained with a PS:KOH ratio of 27.5 g: 7.5 g. The oil yield increased from 80 to 95 wt% when the mass of the catalyst increased from 5 to 7.5 g. On the other hand, the gas yield (3–18 wt%) varied significantly and char yield (1–2 wt%) was not influenced. The yields predicted by ML matched well with the experimental yields. This study demonstrated the potential of KOH as a catalyst for PS pyrolysis technology as the formation of aliphatic hydrocarbons in the oil fraction was significantly promoted. © 2022 The Institution of Chemical Engineers
Multiobjective temperature trajectory optimization for unseeded batch cooling crystallization of aspirin
(Elsevier Ltd, 2022) Ashraf, A.B.; Sankar Rao, C.S.
Batch cooling crystallization is a type of crystallization wherein supersaturation is brought about by reducing the temperature of the crystallization system with time. It is commonly used in the chemical and pharmaceutical industries to manufacture a wide variety of crystalline products. This work deals with multiobjective optimization of unseeded batch cooling crystallization of Aspirin. A novel method involving temperature changes rather than temperatures of the crystallization mixture over time has been discussed in this study. Optimization studies were carried out to minimize the coefficient of variation and maximize mean size. Optimization was carried out using the benchmark NSGA-II and NSGA-II hybrid optimizers available in MATLAB. A standard algorithm to select a trade-off point on the Pareto front is also discussed. Rigorous simulation studies were carried out to determine the best temperature trajectory by inspecting the crystal size distributions generated using the method of characteristics. © 2022 Elsevier Ltd
Novel strategies for glucose production from biomass using heteropoly acid catalyst
(Elsevier Ltd, 2020) Nayak, A.; Pulidindi, I.N.; Sankar Rao, C.S.
Bioethanol and direct glucose fuel cells pledged clean energy to the world. Cellulose depolymerization for glucose production has been a successful approach in bioethanol production. Heteropoly acids (HPAs) are strong Brønsted solid acid catalysts for biomass hydrolysis. Keggin type HPAs, namely, Silicotungstic acid (HSiW), Phosphotungstic acid (HPW), and Phosphomolybdic acid (HPMo), were used for the hydrolysis of lignocellulosic biomass to glucose. Five different biomass feedstocks, namely, miscanthus, sugarcane leaves, switchgrass, sunflower seeds, and bamboo leaves, were examined for the feasibility of total reducing sugar (TRS) yield through the composition analysis and catalytic biomass hydrolysis. Sunflower seeds contained the maximum holocellulose with 90.6%, and switchgrass contained the least i.e., 77.63%. Among the five biomass tested, switchgrass resulted in the highest TRS (5.77 wt/dry wt. %) with HPMo catalyst at a catalyst to biomass ratio of 30:100 (wt./wt. %), a reaction temperature of 120 °C for 3 h. The reaction parameters for depolymerization were optimized for all three HPAs, and the optimized conditions were 3 h and 120 °C. HPMo showed maximum TRS yield (5.77 wt/dry wt.%) among the three HPAs at 30:100 catalyst to biomass ratio. However, a catalyst to biomass ratio of 20:100 (wt./wt.%) was economical (5.25 wt/dry wt.%) for commercial application. © 2020 Elsevier Ltd
Optimal detuning of multivariable proportional integral controller based on data-driven approach for an activated sludge process
(John Wiley and Sons Ltd, 2023) Anchan, S.S.; Kumar Tanneru, H.; Sankar Rao, C.S.
The study deals with designing a multivariable centralized proportional-integral (PI) controller for an activated sludge process (ASP) by adopting a new tuning technique. To overcome the tedious task of manual tuning by a classical method that was based on Ziegler–Nichols tuning, a novel approach is developed by formulating the optimization problem and considering it along with the theoretical results to calculate the controller parameters. The proposed method is then compared with the reported method to demonstrate the advantages of the proposed approach. A nonlinear ASP model is selected to evaluate the performance of the designed controller. The simulation carried through the proposed optimization technique exhibits a significant improvement in closed-loop performance for tuning the centralized controller. It is observed that the proposed method minimizes the effort of tuning the controller significantly. The proposed controller achieves better load disturbance response and lower overshoot than the reported method. The proposed method could improve integral absolute error (IAE) and integral time absolute error (ITAE) performance indices, ranging between 18.69% to 53.80% and 16.42% to 52.97% for a step change in (Figure presented.) and (Figure presented.), respectively, which are better compared to the reported method. The study also assesses the control system's robustness for their uncertainties with the time constant and time delay. © 2023 Curtin University and John Wiley & Sons Ltd.
Prosopis juliflora valorization via microwave-assisted pyrolysis: Optimization of reaction parameters using machine learning analysis
(Elsevier B.V., 2023) Suriapparao, D.V.; Rajasekhar Reddy, B.R.; Sankar Rao, C.S.; Jeeru, L.R.; Kumar, T.H.
Microwave power and pyrolysis temperature are essential parameters in optimizing the bio-oil yield and quality in microwave pyrolysis. This study focused on understanding the interactions between the microwave power/heating rate and pyrolysis temperature in microwave-assisted pyrolysis of Prosopis juliflora. For optimum bio-oil yield, a discrete set of microwave powers (280 W, 420 W, and 560 W) and pyrolysis temperatures (200 °C, 350 °C, and 500 °C) were selected. A central composite design (CCD) was adopted to analyze the effect of microwave power and the pyrolysis temperature on product yields, heating rate, microwave conversion efficiency, and heat losses in pyrolysis. Moreover, the effect of heating rate, reaction time, specific microwave power, specific microwave energy, and conductive heat loss on gas, char, and liquid yields was evaluated using statistical machine learning techniques. Moreover, a new parameter, pyrolysis index, is calculated under different conditions to understand the extent of pyrolysis intensity using pyrolysis time, heating value, feedstock mass and conversion, and microwave energy conversion. The yields of bio-oil, biochar, and gas were 25–40 wt%, 25–35 wt%, and 35–40 wt% at different experimental conditions. Bio-oil consists of a mix of organic compounds with methoxy phenols at high selectivity, and the calorific value of bio-oil was in the range of 26–28 MJ/kg. Carbon number analysis revealed higher presence of C5–C9 compounds. This study shows the role of machine learning in understanding the effect of various parameters effectively and optimizing the experimental conditions accordingly. © 2022 Elsevier B.V.
Robust decentralized proportional–integral controller design for an activated sludge process
(John Wiley and Sons Ltd, 2020) Anchan, S.S.; Sankar Rao, C.S.
This paper presents the design of a decentralized proportional–integral (PI) controller for a wastewater treatment plant (WWTP). The aeration rate and the return recycle sludge rate are manipulated inputs to the WWTP process, while substrate and biomass concentration are considered as the output variables. The study is divided into two segments: A decentralized controller is designed based on the best pairing in the first segment, and in the second segment, a decoupler is developed to reduce the interactions. Decouplers are generally used to create independent loops in the multivariable control loops. Each decoupled subsystem is converted to first order plus time delay (FOPTD) model using a system identification toolbox to design independent diagonal controllers. The numerical simulations have been performed to evaluate the effectiveness of the presented methods. Furthermore, a robustness study has also been carried out by taking into account multiplicative input and output uncertainties, and it is found that the controller designed based on minimizing the integral of absolute error (IAE) criteria shows more robust. © 2020 Curtin University and John Wiley & Sons, Ltd.
Simultaneous separation of ternary mixture using modified dual compression middle vessel batch distillation column: Control and dynamic optimization
(Taiwan Institute of Chemical Engineers, 2022) Desikan, B.; Krishna, P.; Sankar Rao, C.S.
Background: Multivessel batch distillation has been found to be an effective method for the separation of multi-component mixtures. In this article, an effort has been made to devise a fast middle vessel batch distillation column (MVBDC) for the ternary separation of an Ethanol/Propanol/Butanol mixture by the means of inducing vapor compression in the system. Methods: ASPEN PLUS V12 has been used to generate the initial steady-state flowsheet of the process for equipment sizing. In contrast, ASPEN Dynamics was used to evaluate the performance of the batch distillation with various control structures and to perform a dynamic optimization on the proposed batch distillation column. MATLAB was used to identify single-input single-output transfer functions for more effective PID controller tuning. Significant Findings: The proposed Middle vessel batch distillation was found to separate each component of the mixture to a purity of 99 mol% in 22 h (for the cascade control structures) and 21.73 h (for the temperature control structure). This was found to be significantly lesser than the batch time of a conventional batch distillation column (28.12 h), while the energy consumed by the proposed column was 3.4 MMkcal lesser than the energy consumed by the conventional column. Dynamic optimization further reduced the batch time by 14.4% while simultaneously reducing the energy consumed by 20.3%. © 2022 Taiwan Institute of Chemical Engineers
Synthesis of renewable carbon biorefinery products from susceptor enhanced microwave-assisted pyrolysis of agro-residual waste: A review
(Institution of Chemical Engineers, 2022) Rajasekhar Reddy, B.R.; Sridevi, V.; Kumar, T.H.; Sankar Rao, C.S.; Palla, V.C.S.; Suriapparao, D.V.; Undi, G.S.
Valuable renewable carbon biorefinery products can be obtained by using agro-residual biomass as a feedstock. Bio-oil, gas, and char products can be obtained from Microwave-assisted pyrolysis (MAP) by converting agro-residual waste. In MAP, the process variables like microwave power, temperature, heating rate, raw materials, susceptors, and catalysts play an important role to alter the product spectrum. The temperature, heating rate, and pyrolysis time can be tuned to obtain the desired products during biomass decomposition. The obtained carbonaceous products can be used as intermediated feedstocks to synthesize a variety of end products. Hence, in this review, the application of MAP for the conversion of agro-residual waste is discussed. Special focus is given to the interaction of microwaves with susceptors. This manuscript provides background, current status, progress, and future scope of MAP technology for waste valorization. The objectives of the review are to address (i) The necessity of environmental protection, (ii) The role of biorefinery in the biomass conversion, (iii) The advancements in the MAP for the resource recovery, (iv) The mechanism of heat generation from microwaves, (v) The effects of process parameters, susceptors, and catalysts in MAP, (vi) The interactions of biomass and susceptors during the pyrolysis, (vii) The formation of valuable renewable carbon products and (viii) The future scope and challenges for the integration of MAP in solid waste management. © 2022 The Institution of Chemical Engineers
Synthesis of sustainable chemicals from waste tea powder and Polystyrene via Microwave-Assisted in-situ catalytic Co-Pyrolysis: Analysis of pyrolysis using experimental and modeling approaches
(Elsevier Ltd, 2022) Suriapparao, D.V.; Sridevi, V.; Ramesh, R.; Sankar Rao, C.S.; Tukarambai, M.; Kamireddi, D.; Gautam, R.; Dharaskar, S.A.; Pritam, K.
In the current study, catalytic co-pyrolysis was performed on waste tea powder (WTP) and polystyrene (PS) wastes to convert them into value-added products using KOH catalyst. The feed mixture influenced the heating rates (17–75 °C/min) and product formation. PS promoted the formation of oil and WTP enhanced the char formation. The maximum oil yield (80 wt%) was obtained at 15 g:5 g, and the maximum char yield (44 wt%) was achieved at 5 g:25 g (PS:WTP). The pyrolysis index (PI) increased with the increase in feedstock quantity. High PI was noticed at 25 g:5 g, and low PI was at 5 g:5 g (PS:WTP). Low energy consumption and low pyrolysis time enhanced the PI value. Significant interactions were noticed during co-pyrolysis. The obtained bio-oil was analyzed using GC–MS and a plausible reaction mechanism is presented. Catalyst and co-pyrolysis synergy promoted the formation of aliphatic and aromatic hydrocarbons by reducing the oxygenated products. © 2022 Elsevier Ltd