Sustainable Engineering: Process Intensification, Energy Analysis, and Artificial Intelligence

Sustainable engineering is essential for resilient and agile technology and society, and this book integrates process intensification, energy analysis, and artificial intelligence to reduce production costs, improve material and energy use, product quality, safety, societal well-being, and water usage. It covers 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.

Format: Hardback
Length: 402 pages
Publication date: 04 August 2023
Publisher: Taylor & Francis Ltd

Sustainable engineering is of paramount importance for the development of resilient and agile technology and society. This comprehensive book delves into the intricate interplay between economics, the environment, and societal aspects, seamlessly integrating process intensification, energy analysis, and artificial intelligence to achieve cost-effective production, enhanced material and energy utilization, improved product quality, safety, societal well-being, and water efficiency. By leveraging 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, the book offers a thorough exploration of key topics in sustainable engineering.

Process intensification involves enhancing the efficiency of industrial processes by optimizing their design, operation, and control. This includes techniques such as process modeling, simulation, and optimization algorithms, which help identify bottlenecks, improve process performance, and reduce waste. Energy analysis focuses on identifying and optimizing energy sources and consumption in various industrial processes. This includes the study of energy conversion technologies, energy efficiency improvements, and the development of renewable energy sources. Artificial intelligence plays a crucial role in sustainable engineering by enabling the development of intelligent systems that can optimize complex processes, make data-driven decisions, and learn from experience. This includes machine learning algorithms, natural language processing, and predictive analytics, which can help improve process efficiency, reduce costs, and enhance product quality.

Optimization is a fundamental aspect of sustainable engineering, as it helps identify the most efficient and effective solutions to complex problems. This involves the use of mathematical models, statistical analysis, and simulation techniques to evaluate the performance of different systems and processes. Energy integration involves the integration of renewable energy sources, such as solar, wind, and geothermal power, into industrial processes to reduce greenhouse gas emissions and promote sustainability. Green engineering is a multidisciplinary approach that focuses on designing and developing environmentally friendly products and processes. This includes the use of sustainable materials, the reduction of waste and pollution, and the optimization of energy and water consumption. Pinch analysis is a technique used to optimize the use of resources in industrial processes by identifying the points of maximum utilization and minimizing waste. Exergy analysis is a method used to evaluate the efficiency of energy conversion processes and identify opportunities for improvement. Feasibility analysis is a process used to assess the practicality and viability of proposed projects and technologies. Life cycle assessment is a method used to evaluate the environmental impact of products and processes throughout their entire life cycle. Circular economy is a business model that focuses on reducing waste and maximizing resource utilization by promoting the reuse, recycling, and repurposing of materials. Bioeconomy is a field that combines biology, chemistry, and engineering to develop sustainable solutions for agriculture, energy, and other industries. Data processing is a critical aspect of sustainable engineering, as it enables the collection, analysis, and interpretation of large amounts of data to inform decision-making and improve process efficiency. Machine learning is a subset of artificial intelligence that enables computers to learn from data and improve their performance over time. Expert systems are computer programs that mimic the expertise of human experts in a particular domain, such as engineering or medicine. Digital twins are virtual replicas of physical systems that can be used for simulation, analysis, and optimization. Self-optimized plants are industrial systems that are designed to continuously improve their performance by learning from data and adapting to changing conditions.

In conclusion, sustainable engineering is a multidisciplinary field that requires the integration of economics, the environment, and societal aspects to develop innovative and sustainable solutions to complex problems. By leveraging process intensification, energy analysis, and artificial intelligence, sustainable engineering can help reduce production costs, improve product quality, enhance safety, promote societal well-being, and conserve natural resources. The book provides a comprehensive discussion of key topics in sustainable engineering, including 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. By studying and applying these principles, engineers and scientists can contribute to the development of a more sustainable and resilient world.

Dimension: 254 x 178 (mm)
ISBN-13: 9781032042404