Data-driven approach for synthesizing, characterization, and experimental investigation of biomass-based plastic

Abstract

Climate change, an urgent and significant concern in our current stage of human civilization, is exacerbated by the role of synthetic plastic and polymers. Their petrochemical-based synthesis and non-biodegradable nature pose alarming threats. Synthetic polymers, including plastic, are known to be dangerous materials that impact human civilization in various ways. The irreversible covalent crosslink in polymer imparts better mechanical properties (thermal and chemical resistance), reducing the possibility of recycling and reusing chemically synthesized polymer. The study reports that ∼60000 plastic bags are used every 5 seconds. The need to address the detrimental impacts of polymer biomass-based plastic materials is urgent. These materials offer a more sustainable alternative with their superior quality and biodegradability. This research utilizes soya waste to develop sustainable environmental biomaterials, emphasizing the pressing need for immediate action to combat plastic pollution. This article presents an innovative, data-driven approach for synthesizing, characterizing, and experimentally investigating biomass-based plastic. The use of cutting-edge Artificial Intelligence (AI) based tools like Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), as well as statistical approach Response Surface Methodology (RSM), were adopted in this research work, marking a novel and exciting development in the field. These tools were used to develop a model that automates synthesizing biomass-based plastic. The data validation indicates a mean absolute error in water absorption of ∼1.40 % while in methanol, 0.87 %. The synthesized soy-based bioplastic was analyzed instrumentally through FTIR, DTA, and TGA, which yielded satisfactory results. Other physical properties like solubility, biodegradability, and chemical reactivity were also studied in the laboratory. The combination of soy, corn, glycerol, vinegar, and water was optimized using the Response Surface Methodology (RSM). This unique combination was chosen for its potential to produce a high-quality, biodegradable biomass-based plastic, thereby contributing to the development of sustainable materials.