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Membrane bioreactors (MBRs) have proven to be an extremely effective wastewater treatment process combining ultrafiltration with biological processes to produce high-quality effluent. However, one of the major drawbacks to this technology is membrane fouling – an inevitable process that reduces permeate production and increases operating costs. The prediction of membrane fouling in MBRs is important because it can provide decision support to wastewater treatment plant (WWTP) operators. Currently, mechanistic models are often used to estimate transmembrane pressure (TMP), which is an indicator of membrane fouling, but their performance is not always satisfactory. In this research, existing mechanistic and data-driven models used for membrane fouling are investigated. Data-driven machine learning techniques consisting of random forest (RF), artificial neural network (ANN), and long-short term memory network (LSTM) are used to build models to predict transmembrane pressure (TMP) at various stages of the MBR production cycle. The models are built with 4 years of high-resolution data from a confidential full-scale municipal WWTP. The model performances are examined using statistical measures such as coefficient of determination (R2), root mean squared error, mean absolute percentage error, and mean squared error. The results show that all models provide reliable predictions while the RF models have the best predictive accuracy when compared to the ANN and LSTM models. The corresponding R2 values for RF when predicting before, during, and after back pulse TMP are 0.996, 0.927, and 0.996, respectively. Model uncertainty (including hyperparameter and algorithm uncertainty) is quantified to determine the impact of hyperparameter tuning and the variance of extreme predictions caused by algorithm choice. The ANN models are most impacted by hyperparameter tuning and have the highest variability when predicting extreme values within each model’s respective hyperparameter range. The proposed models can be useful tools in providing decision support to WWTP operators employing fouling mitigation strategies, which can potentially lead to better operation of WWTPs and reduced costs. / Thesis / Master of Applied Science (MASc)

<p>Diss. (sammanfattning) Umeå : Umeå universitet, 1994, härtill 5 uppsatser.</p> / digitalisering@umu

This dissertation reports on research related to ultrafiltration (UF) membranes in drinking water applications. A pilot-scale investigation identified seasonal surface water quality impacts on UF performance and resulted in the development of a dynamic chemically enhanced backwash protocol for fouling management. Subsequent analysis of UF process data revealed limitations with the use of specific flux, transmembrane pressure (TMP), and other normalization techniques for assessing UF process fouling. A new TMP balance approach is presented that identifies the pressure contribution of membrane fouling and structural changes, enables direct process performance comparisons at different operating fluxes, and distinguishes between physically and chemically unresolved fouling. In addition to the TMP balance, a five component optimization approach is presented for the systematic improvement of UF processes on the basis of TMP variations. Terms are defined for assessing process event performance, a new process utilization term is presented to benchmark UF productivity, and new measures for evaluating maintenance procedures are discussed. Using these tools, a correlation between process utilization and operating pressures was established and a sustainable process utilization of 93.5% was achieved. UF process capabilities may be further enhanced by pre-coating media onto the membrane surface. Silicon dioxide (SiO2) and powdered activated carbon (PAC) are evaluated as precoating materials, and the applicability of the TMP balance for assessing pre-coated membrane performance is demonstrated. The first use of SiO2 as a support layer for PAC in a membrane pre-coating application is presented at the laboratory-scale. SiO2-PAC pre-coatings successfully reduced physically unresolved fouling and enhanced UF membrane organics removal capabilities.