An efficient healthcare analysis model for selecting optimum configurations of Pyrolytic Products using Deep Learning Model Analysis

Abstract

Pyrolytic products optimization for material strength, thermal stability, and durability optimizations deserves an approach that is highly developed and can cross-disciplines, considering varieties of data across domains and scales. The paper proposes a new framework that incorporates data fusion and bioinspired computing methods in the design chain for enhancing performance and effectiveness in pyrolytic materials. The proposed methodology will be comprehensive, providing different innovative computational methods that will address various challenges of the optimization process. We have developed, for effective dimensionality reduction in high-dimensional pyrolytic data, a Neuroevolutionary Sparse Feature Selector (NSFS), considering only the most critical features of interest, such as chemical composition, particle size distribution, heating rates, residence time, and reactor configurations. TL-MSF integrates data from molecular simulations, laboratory experiments, and field-scale observations into one dataset and helps to enhance the accuracy of the prediction. The AEOS system aims at balancing dynamically such conflicting goals as the maximization of char yield and minimization of tar production and energy consumption in the case of multiple objective optimizations. The base learners are combined in a multi-objective framework for an optimization of pyrolysis parameters, including temperature, pressure, and feedstock type. A Bioinspired Ant Colony Self-Organizing Map (ACO-SOM) is used to cluster the data and find anomalies; doing so can ensure the data on which one grounds the optimization will be reliable. In this work, a Neuro-Fuzzy Real-Time Decision Support System will be implemented to continuously monitor the process of pyrolysis and control it by dynamic adaptation of decision rules based on real-time data samples. This will ensure consistent product quality and operational efficiency through optimization of key pyrolysis characteristics, including volatile release rates, carbon content, and thermal degradation behavior. The integrated approach shows significant process gains of 25-30% increased predictive accuracy, a 15% increase in char yield, and a 20% reduction in environmental impact. This may suggest that cross-disciplinary data fusion and bioinspired computing are fairly effective in optimizing pyrolytic products for material strength, thermal stability, and durability optimizations.