Institute of Chemical Technology (ICT) is a state-funded deemed university in Mumbai, India. The institute also has campuses at Bhubaneswar, Odisha and Jalna, Marathwada. It is focused on training and research in fields of chemical engineering, chemical technology, and pharmacy. It was established in 1933 and was granted deemed university status in 2008, making it the only state-funded deemed university in India. On 12 February 2018 it was given status of Category 1 institute with graded autonomy by MHRD and UGC. It is also an institute with a special status as mentioned in SECTION IV of the Report of the Empowered Expert Committee in 2018.
The present study aims to develop a machine learning-based framework for predicting and optimizing the photocatalytic degradation efficiency of methylene blue (MB) using TiO₂, Fe₂O₃, and Ti–Fe composite nanomaterials. A dataset comprising 706 experimental observations was compiled from literature sources, incorporating key reaction parameters such as catalyst type, catalyst loading, light intensity, solution pH, and reaction time. Various machine learning models, including linear, kernel-based, and ensemble methods, were evaluated for predictive performance. The results demonstrate that ensemble tree-based models significantly outperform conventional approaches in capturing the complex nonlinear relationships governing photocatalytic degradation. Among all models, Light Gradient Boosting Machine (LGBM) achieved the best predictive performance, which further improved after hyperparameter optimization, reaching a test R² of 0.8886 and RMSE of 9.71. HistGradientBoosting and CatBoost also showed competitive performance, while XGBoost demonstrated strong generalization capability with the highest cross-validation score. Feature importance analysis using Random Forest and SHAP revealed that reaction time is the most influential parameter, followed by light conditions and catalyst properties. The findings demonstrate that photocatalytic degradation is governed by complex nonlinear interactions among process variables, which ensemble models effectively capture. The observed trends are consistent with photocatalytic kinetics, in which prolonged irradiation enhances degradation efficiency until equilibrium is reached. Overall, this study highlights the effectiveness of machine learning in modeling and optimizing photocatalytic processes and provides a reliable data-driven framework for methylene blue degradation systems. 18 machine learning models for MB degradation prediction were evaluated. Ensemble boosting models outperform linear and kernel-based approaches. LGBM achieved the highest accuracy (R² ≈ 0.89) with the lowest prediction error. XGBoost demonstrated the strongest generalization under 5-fold cross-validation. Reaction time was identified as the most influential parameter governing degradation.
This study adopts a qualitative lens and explores the mechanisms through which women negotiate internalized sociocultural norms to shape their career aspirations in engineering within the Indian context. Drawing on Pierre Bourdieu’s concept of habitus and symbolic violence, this work investigates the depth of assimilation of gendered beliefs and social conditioning that colour women’s perceptions of their academic potential, leadership capabilities and life trajectories. Content analysis of the narratives from 12 female doctoral students from three premier Indian universities revealed three interconnected themes: “Internalized devaluation of ambition”, “Self-policing and avoidance of high-visibility roles”, and “Perceived incompatibility with gendered life paths”. Findings reveal that these internal mechanisms operate beneath formal institutional structures and perpetuate gender disparity in chemical engineering academia. The study proposes various interventions for mitigating and disrupting this subtle socio-cultural conditioning and advances the theoretical understanding of the unconscious, internalized barriers that impede women’s career progression.
The rapidly depleting non-renewable resources highlight the necessity of searching and incorporating more and more renewable feedstocks for the manufacture of products to suffice the demands of the huge population. Keeping this in mind, an effective methodology was strategized for the hydroformylation and related one-pot processes of the unexplored biomass-derived olefin, linalool oxide. A variety of chemically significant products, such as aldehydes, amines, alcohols, and acetals were obtained through hydroformylation, hydroaminomethylation, hydrohydroxymethylation, and hydroformylation/acetalization, respectively. All the systems were designed to have a high chemoselectivity toward the corresponding products, along with high conversions. To specify, the yields were 100
There is a transition in production of probiotics from milk-based sources to plant-based ones. Even though sucrose-based fermentation offers benefits, the cost of purified sucrose hampers the economics of probiotic production. Sugarcane juice (SJ) appears as a cost-effective and renewable feedstock, for such applications; however, its use is restricted owing to huge loads of antimicrobial phenolics. Removal of phenolics from SJ by reverse micellar extraction, makes it a cost-effective feedstock for fermentative production of Lactobacillus plantarum NCIM2083. The physical parameters of fermentation were individually optimized. Media composition was optimized using a two-level experimental design strategy. Plackett–Burman screening design was applied to identify media components having a significant effect on biomass production and their levels were optimized by a rotatable central composite design to achieve a viable cell count of 10.028 ± 0.375 log CFU/mL. These optimizations improved biomass accumulation by 1.7 times. Kinetic modelling of batch fermentation was also attempted for evaluation of the process. Usability of SJ as a suitable feedstock for production of probiotics was successfully demonstrated. This study demonstrates the feasibility of using clarified sugarcane juice in microbial fermentations and opens new horizons for sucrose-based microbial fermentation processes using minimally processed sugarcane juice.
The selective oxidation of lignin feedstock-derived alcohol is a sustainable process for the synthesis of aldehyde. Vanillyl alcohol is used as a model component of lignin feedstock-derived alcohol. Moreover, Cu-impregnated CaAl2O4 spinel catalysts were synthesised and investigated for their catalytic efficiency in the selective oxidation of vanillyl alcohol via thermal and photochemical routes. Copper impregnated into the CaAl2O4 spinel support significantly altered its structural, morphological, and catalytic properties, as confirmed by extensive characterisation techniques such as XRD, Raman spectroscopy, FTIR, XPS, HR-TEM, surface area analysis, LSV, photoluminescence studies, and ICP-AES. 5
