Mukesh Kumar Gupta

India’s solar photovoltaic expansion requires planning tools that can identify broad zones where new ground-mounted projects may merit detailed feasibility assessment. This study develops a GeoAI screening framework that combines cleaned solar-plant reference points, open remote-sensing predictors, machine-learning models, a tabular PyTorch deep-learning model, exclusion masking, spatial validation, external inventory checks, and explainable AI. Using 2848 cleaned utility-scale solar reference points and a 17,088-sample presence-background training matrix on a 0.05-degree grid, the workflow estimated relative solar-development suitability across India. The final four-member ensemble achieved ROC-AUC = 0.834, PR-AUC = 0.591, and F1 = 0.513 under spatial holdout validation; grouped spatial cross-validation produced mean ROC-AUC = 0.776 ± 0.082, PR-AUC = 0.505 ± 0.130, and F1 = 0.406 ± 0.102. After exclusion masking, the ensemble identified 145,391 km² of High or Very High suitability land, including 26,798 km² in the Very High class. Under the baseline 2.0 ha MW⁻¹ land-use assumption, developing 1–10% of the High/Very High screening area corresponded to approximately 73–727 GW of upper-bound planning-scale technical capacity. Independent World Resources Institute (WRI) and Global Energy Monitor (GEM) overlays indicated moderate spatial consistency, with 30.0–48.3% of external records falling in High or Very High classes. Sensitivity testing showed that probability cutoff, cropland treatment, and land-use assumptions strongly affect mapped extent and capacity scenarios, whereas reasonable slope-threshold changes had limited national effect. The results should therefore be interpreted as a reproducible screening and policy-prioritization surface, not as a parcel-level feasibility map.

The present work focused on the application of zirconium dioxide (ZrO2), a metal based nano material in a helical tube heat exchanger. A novel nano enhance phase change material (NEPCM) passes through a series of test to explore its energy storage capacity. The aim of the study is to determine the effect of varying amounts of ZrO2 on the thermal performance of energy storage systems through experiments, and based on the results, to identify the optimal concentration. The scanning electron microscopy (SEM) results indicated that the ZrO2 physically bonded with paraffin wax without disturbing the chemical stability and structure of the NEPCM samples. It was noticed that ZrO2 successfully stabilized surface temperature, solidification and melting of paraffin wax. Experiments with ZrO2 doped with paraffin wax also helped to establish the optimum value of charging and discharging time. The most significant enhancement in charging rate was observed with a mere 0.1–0.3% (vol.) increase, but this enhancement rapidly declined with increases beyond 0.3% in volume concentration. NEPCM with 0.3% concentration in volume was identified as ideal sample. When the mass flow rate increased from 1 to 5 LPM, the exergy efficiency dropped from 21.4% to 15.3%. Compared to other PCM and NEPCM samples, ZrO2-doped PCM samples exhibited a more favorable thermal response.

Because textile industries are intensely water-consuming and generate a huge quantity of wastewater, the present study examines the scope of using solar thermal technology to treat wastewater from textile industries. A hybrid technology, comprising a compound parabolic concentrator-based solar thermal system in conjunction with a Membrane Distillation (MD) system, is experimented with for wastewater treatment in textile industries. The MD system requires a water temperature of around 90 °C for efficient functioning. The advanced MD technology using waste heat combined with solar heat to meet the system’s thermal load is technologically evaluated for an experimental textile industry in India. Moreover, the present study critically analyses the techno economics of the proposed hybrid technology. A detailed financial analysis has revealed that, besides technological superiority, the recommended technology is also financially rewarding for wastewater treatment in the textile industry. To cope with the delayed payback period, financial incentives are recommended so that the system becomes a lucrative technological option.

Objectives: To fix the security issues of devices in internet of things (IOT) systems that arise when Machine learning (ML) and Deep Learning algorithm (DLA) are implemented in the IOT systems. Methods: Each packet of IoT threats has been filtered by using suitable attack model for primary attack detection. In deep learning, network traffic field’s information is extracted from of packet and is used as the training features. Attack is proposed to be detected by the use of database-based features of attack. If attack is found then packet will be discarded along with update feedback to filtering stage for primary attack detection. Knowledge Discovery and Data Mining (KDD) dataset is used in this study. Different types of Mirai attack are considered for classification. The performance of the proposed method has been compared in terms of accuracy and execution time with earlier work using traditional ML-DL methods. Findings: The proposed technique is seen to achieve high accuracy level at the least computational time, concurrently with much higher recall and G-mean values Several algorithms are used to secure the IOT devices from various types of threats and a comparison is depicted with their accuracy and execution time. Results show that the proposed methodology using Convolution neural network (CNN) for classification, can achieve the accuracy ~0.9976 with execution time1.30 sec and G-mean 0.7865 only. Novelty: The proposed method is essentially a judiciously configured ML- DL technique which is novel and exhibits better performance in terms of mitigating IoT threats. Keywords: IOT devices and threats; Machine Learning; Deep Learning; KDD; Security and privacy