Today's industrial landscape is marked by rising energy costs and an increased focus on sustainability. Companies, keen on projecting a green corporate image, are constantly looking for ways to make their production processes more energy-efficient. One such domain is heat treatment, where energy efficiency isn't just about cost-saving; it is also about reducing CO2 emissions and meeting stringent legal and environmental standards. The motivation for this thesis is the need to enhance energy efficiency in industrial processes. With rising energy costs and stringent environmental regulations, industries are compelled to adopt more sustainable practices. Heat treatment processes, vital in many sectors including battery production, consume substantial energy. Optimizing these processes can lead to significant energy savings and environmental benefits. The methodology involves a literature review and the development of a simulation model. The thesis reviews existing literature on heat treatment processes and energy-saving potentials. A simulation model of a drying chamber is constructed, simulating sensor accuracy and monitoring energy consumption data. The thesis explores energy-focused loading rules and scheduling algorithms, such as genetic algorithms and the Strength Pareto Evolutionary Algorithm 2, to optimize production processes. The simulation model is validated with a real-world case and applied to various scenarios to evaluate energy-saving potentials. The research reveals that energy-focused loading rules and advanced scheduling algorithms can impact energy consumption. Algorithms optimize job scheduling by considering multiple objectives, reducing peak energy demand and spreading usage evenly, minimizing energy waste, and enhancing overall efficiency. Sensor accuracy plays a major role in the simulation model. Higher sensor accuracy improves the precision of energy estimations, reducing rework. However, the relationship between sensor accuracy and energy consumption can be complex. In conclusion, integrating advanced algorithms, digital tools, and precise sensor data into industrial processes is crucial for enhancing energy efficiency. Continuous improvements in sensor technology will drive further efficiency gains, enabling industries to achieve significant energy savings, reduce environmental impact, and align with sustainability goals.
Energy reduction of heat treatment by production scheduling and sensor data via simulation
Hochradner, A. (Author). 2024
Student thesis: Master's Thesis