Hitesh Panchal

Dharmsinh Desai University

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Suresh Muthusamy

Tumkur University

Kishor Kumar Sadasivuni

Qatar University

Ammar H. Elsheikh

Lebanese American University

Ravishankar Sathyamurthy

King Fahd University of Petroleum and Minerals

Hagar Alm‐Eldin Bastawissi

Tanta University

S. Shanmugan

Koneru Lakshmaiah Education Foundation

Medhat Elkelawy

Tanta University

Ali Jawad Alrubaie

Al-Furat Al-Awsat Technical University

Chandrakant Sonawane

Symbiosis International University

Fadl A. Essa

Kafrelsheikh University

Cooperative Institutions

Anna University, Chennai

SRM Institute of Science and Technology

Madurai Medical College

Tanta University

Hong Kong College of Technology

Gujarat Cancer & Research Institute

Saveetha University

PSG INSTITUTE OF TECHNOLOGY AND APPLIED RESEARCH

Vellore Institute of Technology University

Savitribai Phule Pune University

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Numerical analysis of the powder mixed electrical discharge machining (PMEDM) process for TZM-molybdenum superalloy using finite element method

AIP Advances (2024)

Kapil Surani Shailesh Patel Mathan Kumar Mounagurusamy Musaddak Maher Abdul Zahra Hitesh Panchal

The powder mixed electrical discharge machining (PMEDM) process was simulated via finite element analysis in the current study to assess heat behavior and material removal rate. The goal of this paper is to conduct a thorough experimental and thermal examination of electrical discharge machining (EDM) in order to forecast its cutting characteristics and subsequently optimize the output variables using a response surface methodology for simulations and choosing the most suitable set of process variables related to the PMEDM process. This study’s objective is to design a 2D axisymmetrical transient thermal model that might also describe the physics of material removal in a single spark PMEDM operation on a Titanium Zirconium Molybdenum (TZM) superalloy. ANSYS (version 9.1) software is used to perform transient heat transfer simulations to determine the temperature profile with the amount of material removal at different current, pulse on and off times, gap voltages, and fraction of heat that enters the specimen. The PMEDM process produced craters with a lower diameter and depth, which increased the material removal rate and enhanced the surfacing quality. Compared to the conventional EDM process, the inclusion of powder raised the heat flux given to the work material by 10%–12%. It has been determined that with the single spark modeling technique, the temperature significantly dropped in both the radial and depth directions. The computational results are compared with experimental observations for similar machining conditions, and both results agree satisfactorily.

Electrical Discharge Machining

10.1063/5.0188809

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An Improved Quality Inspection of Engine Bearings Using Machine Vision Systems

Smart and Sustainable Manufacturing Systems (2022)

S. Vivek K. Srinivasan B. Sharmila Y. Dharshan Hitesh Panchal

Abstract The growth of an industry is mostly based on the quality of the product. When there is a fault in the manufactured product in a batch, the entire batch is rejected because of a single faulty product. Quality has become one of the important factors deciding the growth of the industry. Industries nowadays are being automated in every phase of the manufacturing process. Here, an automated system is developed for the inspection of the product developed for the automobile industry. The bearing is one of the parts that play a major role in the connections with the engine and the shafts. Therefore, the bearing has to be perfect, as faulty bearings will lead to greater damage regardless of others. In the automated system, the bearing is inspected for the missing operations in the product. The faulty product is rejected and would be checked for rework or to be scraped. The inspected products are then being sent for the packing process. Through the automated inspection system, the production rate of the product can be increased, also increasing the reputation of the industry. The objective of the present work is to develop a machine vision system for quality inspection of bearing efficiency. The LabVIEW-based approach is carried out for the implementation in current research work.

Machine Vision

Automated optical inspection

10.1520/ssms20210012

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Citations (1)

Performance analysis of conventional triple basin solar still with evacuated heat pipes, corrugated sheets and storage materials

Groundwater for Sustainable Development (2020)

Mit Patel Chetankumar Patel Hitesh Panchal

Solar still

10.1016/j.gsd.2020.100387

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Citations (50)

Annual performance analysis of adding different nanofluids in stepped solar still

Journal of Thermal Analysis and Calorimetry (2019)

Hitesh Panchal Ravishankar Sathyamurthy A.E. Kabeel S.A. El‐Agouz D. Dsilva Winfred Rufuss

Solar still

10.1007/s10973-019-08346-x

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Citations (91)

Fuel Extraction from Plastic Waste

International Journal for Research in Applied Science and Engineering Technology (2025)

Hitesh Panchal

The extraction of fuel from plastic waste through the pyrolysis process offers a promising solution to address both environmental concerns and energy needs. In this project, a horizontal furnace setup is employed to conduct pyrolysis, utilizing a quartz tube containing small plastic pieces. The process involves raising the temperature within the furnace to initiate pyrolysis, leading to the breakdown of plastic polymers into valuable fuel components. The extracted fuel is subsequently subjected to testing to evaluate its properties. Through this innovative approach, we aim to demonstrate the feasibility and effectiveness of converting plastic waste into a valuable energy resource, contributing to sustainable waste management and reducing reliance on fossil fuels. This project is driven by the urgent need to reduce the harm that plastic waste is causing to marine life and ecosystems. Millions of tonnes of plastic are produced each year, making the disposal of plastic waste an urgent global concern. Using pyrolysis to turn plastic trash into useful fuel resources, this project aims to reduce environmental damage and promote sustainable energy sources.

Plastic Waste

10.22214/ijraset.2025.66489

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Enhancement of the yield of solar still with the use of solar pond: A review

Heat Transfer (2020)

Hitesh Panchal Kishor Kumar Sadasivuni Fadl A. Essa Sengottain Shanmugan Ravishankar Sathyamurthy

Abstract A solar pond (SP) is a remarkable growth of renewable energy technology that has stored solar energy for storage purposes and is used in many solar thermal applications. It is also utilized for many purposes, such as heating, cooling, space heating, air conditioning, and many more. The present paper shows the use of an SP to improve the yield of solar still (SS) by providing hot water through the heat energy stored in it. It also reveals the use of shallow and mini SPs with SS to improve yield. Various future research works on SS using SPs have also been included in this paper. From the current review paper, it was concluded that the SP increases the yield of the SS.

10.1002/htj.21935

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Citations (85)

An experimental investigation of emission performance of heterogenous catalyst jatropha biodiesel using RSM

Case Studies in Thermal Engineering (2021)

Aparna Singh Shailendra Sinha Akhilesh Kumar Choudhary Deepak Sharma Hitesh Panchal

Tremendous growth in the number of automobiles in developed and developing global economies has exorbitantly boosted competition for petroleum products. Petroleum products derived from fossil fuels are predominantly responsible for environmental pollution as unburnt hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx) & carbon dioxide (CO2) emissions are released from the fossil fuel combustion. In the view of increasing environmental pollution and stringent emission norms, the present study is concentrated on using Jatropha biodiesel as an alternate fuel source to run variable compression ratio (VCR) diesel engine. The characteristics of VCR diesel engine emission have been evaluated under different compression ratio (CR), operating conditions of load & pressure of fuel injection. In this research work, Jatropha biodiesel diesel blend B30 (30% biodiesel and 70% diesel) and B0 (100% diesel) have been taken as fuel to run the engine. For conducting experiments, load has been varied from 0 to 12 Kg, CR from 14 to 18 and FIP from 180 to 270 bar as per the model of Response Surface Methodology experiments. The experimental investigation showed that the use of the B30 blend reduces HC & CO emissions by about 16.7% and 24% correspondingly in comparison to diesel. However noteworthy rise in NOx & CO2 emissions rate recorded by using the B30 blend as that of diesel. It has been shown that with enhancing in load & CR, HC&CO emissions decreased significantly however increase in CO2 and NOx observed. Advancing FIP, significantly decreases HC & CO emissions as well as tends to increase NOx and CO2 emissions.

Jatropha

10.1016/j.csite.2021.100876

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Citations (50)

A novel method for image captioning using multimodal feature fusion employing mask RNN and LSTM models

Soft Computing (2023)

Kumaravel Thangavel Natesan Palanisamy Suresh Muthusamy Om Prava Mishra Suma Christal Mary Sundararajan

Closed captioning

Feature (linguistics)

10.1007/s00500-023-08448-7

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Citations (14)

Machine learning approaches for real-time forecasting of solar still distillate output

Environmental Challenges (2023)

Deepak Kumar Murugan Zafar Said Hitesh Panchal Naveen Kumar Gupta S. Sekar

Solar stills provide a promising avenue for freshwater production in regions grappling with water scarcity, especially remote locales. However, their efficiency is often constrained by the variable climatic conditions. Conventional prediction methods fall short in consistently forecasting the yield, leaving a significant gap in optimizing solar still operations. Recognizing this, the introduction of machine learning becomes pivotal. With a robust predictive model, operators can avoid inefficiencies, inconsistent outputs, and sub-optimal resource utilization. The primary objective of this research is to determine the most suitable machine learning model tailored for predicting solar still output under specific environmental conditions. This research work assessed various machine learning models, including linear regression, decision trees, random forest, support vector machines, and multilayer perceptron. Evaluation metrics encompassed Mean Absolute Error (MAE), cross-validation, grid search, and randomized search techniques. Our results identified the Decision Tree model, registering a MAE of 5.43 and 5.74 through random and grid search methods, respectively, as the preeminent predictor for our dataset. This machine learning-centric methodology elevates the precision of solar still output predictions and paves the way for enhanced solar still designs and superior optimization of solar energy conversion mechanisms.

Hyperparameter Optimization

Multilayer perceptron

Perceptron

Tree (set theory)

10.1016/j.envc.2023.100779

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Citations (14)

Microcontroller PIC 16F877A standard based on solar cooker using PV—evacuated tubes with an extension of heat integrated energy system

Environmental Science and Pollution Research (2021)

Arulraj Simon Prabu V. Chithambaram Maria Anto Bennet S. Shanmugan Catalin I. Pruncu

Cooker

Unavailability

10.1007/s11356-021-16863-2

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Citations (14)