Concentration of hydrogen peroxide and improving its energy efficiency

Distillation is a unit operation of process industry, which is used to separate a liquid mixture into two or more products and to concentrate liquid mixtures. A drawback of the distillation is its high energy consumption. An increase in energy and raw material prices has led to seeking ways to improve the energy efficiency of distillation. In this Master's Thesis, these ways are studied in connection with the concentration of hydrogen peroxide at the Solvay Voikkaa Plant. The aim of this thesis is to improve the energy efficiency of the concentration of the Voikkaa Plant. The work includes a review of hydrogen peroxide and its manufacturing. In addition, the fundamentals of distillation and its energy efficiency are reviewed. An energy analysis of the concentration unit of Solvay Voikkaa Plant is presented in the process development study part. It consists of the current and past information of energy and utility consumptions, balances, and costs. After that, the potential ways to improve the energy efficiency of the distillation unit at the factory are considered and their feasibility is evaluated technically and economically. Finally, proposals to improve the energy efficiency are suggested. Advanced process control, heat integration and energy efficient equipment are the most potential ways to carry out the energy efficient improvements of the concentration at the Solvay Voikkaa factory. Optimization of the reflux flow and the temperatures of the overhead condensers can offer immediate savings in the energy and utility costs without investments. Replacing the steam ejector system with a vacuum pump would result in savings of tens of thousands of euros per year. The heat pump solutions, such as utilizing a mechanical vapor recompression or thermal vapor recompression, are not feasible due to the high investment costs and long pay back times.

Heat Demand Forecasting Models’ Development: Use of Data Mining Tools in SQL Server Analysis Services

This thesis introduces heat demand forecasting models which are generated by using data mining algorithms. The forecast spans one full day and this forecast can be used in regulating heat consumption of buildings. For training the data mining models, two years of heat consumption data from a case building and weather measurement data from Finnish Meteorological Institute are used. The thesis utilizes Microsoft SQL Server Analysis Services data mining tools in generating the data mining models and CRISP-DM process framework to implement the research. Results show that the built models can predict heat demand at best with mean average percentage errors of 3.8% for 24-h profile and 5.9% for full day. A deployment model for integrating the generated data mining models into an existing building energy management system is also discussed.