Construction of Long-Term Transmembrane Pressure Estimation Model for a Membrane Bioreactor

Abstract

A membrane bioreactor (MBR) is equipment which filters polluted water such as factory disposal and sewage. Activated sludge is used to remove organic substances metabolically and filtrated to a membrane by transmembrane pressure (TMP). Since the MBR is able to treat water for a short time and has space-saving features, carrying out distributed installation of the MBR and performing unmanned operation to a building, a factory, and so on, attracts much attention as a solution of water-shortage. However, the rise of transmembrane pressure (TMP) which arises as a result of accumulation of foulants on a membrane is one of the biggest problems. Membrane needs to be washed when TMP reaches to some extent. The focus of this study is to estimate TMP with statistical models and also know when the membrane wash-up will become necessary. In this study, two types of statistical models were constructed between explanatory variables related to fouling and an objective variable, i.e., membrane resistance (R) or deposition rate of foulants to membrane (DR). Partial least squares (PLS) and support vector regression (SVR) were employed for the construction of each model. It is able to predict TMP because R or DR can be converted into TMP. As a result of TMP prediction with real industrial data, usage of DR as an objective variable and the SVR method improved the accuracy of TMP prediction.