Journal Articles
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Item 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. © 2025Item 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.Item 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.Item 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.Item 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 EngineersItem 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 LtdItem 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 EngineersItem 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 LtdItem 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 SocietyItem 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.