Demirel Y. Sustainable Engineering. Process Intensification,...AI 2023 torrent

Textbook in PDF format

Sustainable engineering is of great importance for resilient and agile technology and society. This book balances economics, environment, and societal elements of sustainable engineering by integrating process intensification, energy analysis, and Artificial Intelligence to reduce production costs, improve the use of material and energy, product quality, safety, societal well-being, and water usage. The book provides comprehensive discussion of topics on process intensification, energy analysis, and Artificial Intelligence that include optimization, energy integration, green engineering, pinch analysis, exergy analysis, feasibility analysis, life cycle assessment, circular economy, bioeconomy, data processing, Machine Learning, expert systems, Digital Twins, and self-optimized plants for sustainable engineering.
Industrial Artificial Intelligence (AI) is a systematic, collaborative, and integrative discipline focusing on developing, embedding, and deploying various purpose-oriented Machine Learning algorithms, domain?specific industrial applications with sustainable business value for capital-intensive, process industries. Real-life behaviors of complex interconnected assets, processes and systems are defined by design characteristics and capacity limits. AI operates and manages the asset within the fundamentals of science.
Machine Learning (ML) results from pattern recognition and learning from data, or previous calculations, to develop decisions and results. Machine Learning capability comes from the ability of data analysis that automates analytical model building. Data mining and Bayesian analytics with enhanced computational abilities can produce models that can analyze complex data and produce accurate results for process improvements and better business opportunities. Therefore, ML acts as a subset of AI to train a machine or a process how to learn and manage data toward automated analytical model building.
Sustainable Engineering.
Environmental Sustainability.
Economic Sustainability.
Societal Sustainability.
Sustainability Metrics.
Process Intensification.
Energy Analysis.
Artificial Intelligence.
Workforce in Sustainable Engineering