Defence in the field of mechanical engineering M.Sc. (Tech) Behnam Talebjedi

Opponent Professor Elin Svensson, Chalmers University of Technology, Sweden

Custos Professor Sanna Syri, Aalto University, School of Engineering, Department of Mechanical Engineering

This thesis is based on industrial projects with Pulp and Paper industry in a Nordic country. The main focus of the thesis is on the energy efficiency development of the thermomechanical pulp (TMP) mill and optimal integration of the TMP mill and paper machine through heat recovery and the concept of an Energy Hub. Advanced statistical approaches and machine learning methods have been employed to develop refining identification models and advanced energy-saving refining optimization methods for the TMP process. The developed optimization strategy based on the integration of Machine learning methods and Genetic optimization algorithm confirms an average reduction of 14 % for the total refining-specific energy consumption. In the following, the optimal integration of the TMP mill and paper machine has been investigated through the Energy Hub (EH) concept. The proposed approach for the cost and energy-efficient design and operation of EH is based on the integration of thermo-economic analysis, reliability and availability analysis, and EH load prediction. The proposed design method offers a robust design that isn't impacted by penalty rates of unsupplied demand. Depending on the penalty rates, the total system cost could decrease by 14%-28% utilizing the proposed design method.