Process modelling to facilitate model-based decision-making for resource recovery from urban wastewater - A grey-box approach applied to nanofiltration

Abstract

Currently, decision support tools (DSTs) for wastewater treatment and resource recovery from wastewater use oversimplified databases evaluate and design of treatment trains. The databases consist of only the average, minimum, and maximum process performances, whereas most processes perform differently depending on the process characteristics and operating conditions. To address this issue of oversimplification, this study demonstrates how a grey-box modelling approach for nanofiltration (NF) can serve as an alternative to extensive databases. The membrane model used in this study is a modified version of the solution-diffusion imperfection model proposed by Niewersch et al. (2020). This model was used to estimate water and solute permeabilities (chemical oxygen demand (COD), total nitrogen (TN), and total phosphorous (TP)) based on flux and solute removal literature data at various transmembrane pressures (TMPs; between 4 and 24 bar) for the two membranes, Dow NF90 and NF270. The estimated parameters were cross-validated to predict flux and solute removal. The validation mean absolute percentage errors were below 20 % in most cases, except for the TN rejection, which was 51 %. The applicability and relevance of the NF model were then evaluated using an optimisation model aimed at meeting recovery targets and simultaneously minimising costs (operational and capital expenditure defined by the membrane area and the pumping power, respectively). The optimisation results showed that the selection of an NF membrane (NF90 or NF270) and the operating condition (TMP) were sensitive to the resource recovery targets. In conclusion, a grey-box model can potentially improve the performance of DSTs for resource recovery from wastewater.