Achieving sustainability in heat drying processing: Leveraging artificial intelligence to maintain food quality and minimize carbon footprint

The food industry is a significant contributor to carbon emissions, impacting carbon footprint (CF), specifically during the heat drying process. Conventional heat drying processes need high energy and diminish the nutritional value and sensory quality of food. Therefore, this study aimed to investigate the integration of artificial intelligence (AI) in food processing to enhance quality and reduce CF, with a focus on heat drying, a high energy-consuming method, and offer a promising avenue for the industry to be consistent with sustainable development goals. Our finding shows that AI can maintain food quality, including nutritional and sensory properties of dried products. It determines the optimal drying temperature for improving energy efficiency, yield, and life cycle cost. In addition, dataset training is one of the key challenges in AI applications for food drying. AI needs a vast and high-quality dataset that directly impacts the performance and capabilities of AI models to optimize and automate food drying.

Optimization and characterization of hybrid bio-briquettes produced from the mixture of sawdust, sugarcane bagasse, and paddy straw

Biomass briquetting is a viable densification technique that converts waste biomass materials into useful products and alternative energy. This work explores the characteristics and optimization of hybrid bio-briquette production by combining crop residues (paddy straw) and solid biomass materials (sawdust and sugarcane bagasse). A total number of 20 briquettes were fabricated with three input factors: sawdust (SD), sugarcane bagasse (SB), and paddy straw (PS) based on the faced-centered central composite design (FCCCD) approach in the laboratory to investigate the calorific value (CV) and ash content (AC). The bomb calorimeter technique was used to evaluate the briquette's calorific value and ash content. The proposed work focused on optimizing the briquette input parameters (SD, SB, and PS) and output responses (CV and AC) using analysis of variance (ANOVA) and response surface methodology (RSM) and hybrid artificial neural network-integrated with multi-objective genetic algorithms (ANN-MOGA). This study shows that the MOGA-ANN-based model results in the best value of CV (17.07 MJ/kg) and AC (1.95%) with optimal input parameters SD (39.99 g), SB (29.02 g), and PS (69.02 g). The optimal results observed from the MOGA-ANN model have also been validated experimentally. The Fourier transform infrared (FTIR) spectroscopy investigation reveals that biomass briquettes are the sustainable and environment-friendly option of fossil fuels for power generation and indoor cooking. The study suggests a strategy for minimizing agro-waste, which may be converted into future fuel in the form of briquettes.