Comparison Between a Detailed Pinch Analysis and the ‘Heat Load Model for Pulp and Paper’ – Case Study for a Swedish Thermo-Mechanical Pulp and Paper Mill
Comparison Between a Detailed Pinch Analysis and the ‘Heat Load Model for Pulp and Paper’ – Case Study for a Swedish Thermo-Mechanical Pulp and Paper Mill
Isaksson J., Åsblad A., Berntsson T., 2012, Comparison Between a Detailed Pinch Analysis and the ‘Heat Load Model for Pulp and Paper’ – Case Study for a Swedish Thermo-Mechanical Pulp and Paper Mill, Chemical Engineering Transactions, 29, 43-48.
Pinch analysis has been used for several decades as a tool for making industrial processes more energy efficient by identifying process integration opportunities. Hakala et al. (2008) recognise that pinch analysis is a powerful tool when it comes to improving energy efficiency in mechanical pulp and paper mills, however often very time consuming due to the extensive need for input data. The heat load model for pulp and paper (HLMPP) tool was developed at Aalto University in Finland as a means of providing a flexible tool for a first quick scan of process integration potential. The intention of this study is to evaluate if the model can accurately estimate the data necessary for performing a pinch analysis for a Swedish thermo-mechanical pulp (TMP) and paper mill. Jönsson et al. (2010) used the HLMPP tool to evaluate the potential for steam savings for four Scandinavian TMP mills. It was found that the minimum steam demands were 2-20 % lower than the current consumptions in the mills. In this study, a detailed pinch analysis was carried out for one of the studied mills described by Jönsson (the mill with the lowest energy savings potential according to the HLMPP screening) to identify strengths and shortcomings of the HLMPP tool. An initial comparison shows that the pinch temperature and demand for hot and cold utility predicted by the HLMPP tool, as presented by Jönsson, differs from the detailed pinch analysis. However, further investigation showed that the HLMPP results can be aligned to the detailed data with good accuracy if more time and knowledge about the process is put in to the model.