Contents
Autonomous Water Treatment Systems and Machine Learning (NAWI I)
Background
Program Description and Modeling
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Ultrafiltration Operation and Process Modeling
Dwindling fresh water supplies from traditional sources, along with frequent drought conditions worldwide, intensify the urgency to identify sustainable and alternative sources of drinking water. In recent years, the adoption of reverse osmosis (RO) for seawater and brackish water desalination and water reuse technologies has increased globally. Despite the promising and relatively accessible water treatment option, membrane fouling poses a significant challenge for the effective operation of RO plants treating seawater and brackish water. RO fouling degrades membrane performance by diminishing membrane permeability and subsequently requiring a higher feed pressure to achieve the same desired flux. As a result, the frequency of required chemical cleaning is increased, the membrane lifetime is shortened, and the operational expenditures swell.
Ultrafiltration (UF) has recently emerged as a promising approach for the pre-treatment of the feed that offers a variety of advantages over conventional options including sand filters and cartridge filters. UF membranes, with pore sizes ranging from 0.1 – 0.01 μm, are capable of removing particulates, colloids, microorganisms, and some dissolved organic matter (often with the help of coagulant dosing) that produces high quality permeate water. UF, primarily used for RO feed pretreatment, is typically employed in a dead-end filtration mode with periodic backwash to remove foulants accumulated on the UF filters. This may be enhanced by optimizing the coagulant dosing of the feed and timed backwash frequency. However, UF fouling in a complex phenomenon that is influenced by a myriad of environmental factors (e.g., quality and temperature of feed, and the stochastic nature of the fouling phenomena). Therefore, developing an effective UF process control strategy would benefit from being able to describe the temporality of UF field performance along with quantification of fouling progression. Accordingly, our past work has focused on investigating the complex relationships between various UF operational variables (including coagulant dosing) and UF fouling behavior as observed under field conditions for UF treatment of RO seawater feed (Figure 1.).
Figure 1: Graphical illustration of the approach to describing the dynamics of ultrafiltration (UF) performance in pretreatment of seawater reverse osmosis (SWRO) feedwater was explored via Ensemble Backpropagation Neural Networks (BPNN) model with Alopex Evolutionary Algorithm (AEA).
A machine learning approach to describing the dynamics of ultrafiltration (UF) performance in pretreatment of seawater reverse osmosis (SWRO) feedwater was explored via Ensemble Backpropagation Neural Networks (BPNN) model with Alopex Evolutionary Algorithm (AEA), as illustrated in Figure 2. To address data diversity challenges (i.e., bias-variance tradeoffs) for single machine learning model, an AdaBoost ensemble strategy was followed, developing a family of BPNN-AEA models for the progression of UF membrane resistance during both filtration and backwash, along with backwash efficiency of a real SWRO system with over 13 million data samples collected over the period of 422 days.
Figure 2. Workflow for UF filtration and backwash efficiency assessment and BPNN-AEA model development.
Distributed Water Systems
Water Use Patterns in Small Communities
Small communities with impaired local well water and lack of a feasible connection to a centralized water system can potentially opt for wellhead water treatment as a mid – or long – term solution to providing safe drinking water. However, sufficient data and models of high temporal resolution (hourly to seasonal variability) for forecasting small communities’ water use patterns are critical in order to establish:
- community water system design and operational specifications
- water storage capacity
- water treatment system for upgrading community water quality as needed
- handling of sanitary water
- overall community planning (e.g., expansion and water system infrastructure upgrade).
Intermittent Operation of Wellhead RO Desalination and Water Purification System
Transfer Learning for Updated Operational Model for Upgraded Sysem Components
Transfer Learning for Accelerating Operational Model Development for Distributed Water Systems
Assessment of Membrane Fouling and Mineral Scaling based
Determination of Fundamental
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