Performing Department
(N/A)
Non Technical Summary
Recycling wastewater can help meet the growing demand for food. However, removing harmful heavy metals from wastewater is difficult due to their small size and variety. Current methods to detect these metals are costly and complicated, and the irregular monitoring of filtration systems reduces their effectiveness over time.To solve these issues, we propose developing a new type of membrane that can both monitor and remove heavy metals in real-time. This membrane is made from a new material that can effectively capture various metals and can be reused. It includes sensors that generate signals based on the presence of metals, which are then analyzed by a machine-learning algorithm to identify different metals and their amounts. This data-driven method will help optimize the membrane's operation and maintenance.
Animal Health Component
50%
Research Effort Categories
Basic
30%
Applied
50%
Developmental
20%
Goals / Objectives
This project aims to enhance process flexibility and optimize operational performance by developing integrated sensor-coordinated membrane systems, resulting in in-situ monitoring and removal of heavy metals. Specifically, we propose to develop anti-fouling, regenerable biocomposite adsorption membranes for Me++ removal in wastewater reclamation applications. The membrane-coordinated triboelectric sensor offers real-time data on water quality and treatment performance. Employing data-driven machine learning (ML) supervision, it intelligently optimizes the membrane regeneration process, resulting in enhanced treatmentefficiency and reduced costs.The specific objectives are as follows:Objective #1: Fabricate and optimize biocomposite membraneObjective #2: Evaluate triboelectric responses for metal ion detection and membrane regeneration actuationObjective #3: Evaluate the performance of the sensor membrane in hydroponic systems
Project Methods
Objective #1: Fabricate and optimize biocomposite membraneSubtask 1.1: Manipulate polyamine ligand structure to enhance multiple-metal binding capacity.Subtask 1.2: Synthesize and characterize CA-based crosslinking biocomposite membranes.Subtask 1.3: Evaluate membrane performance (e.g., adsorption, flux, fouling, regeneration)Subtask 1.4: Bayesian optimization (BO)-guided membrane fabrication and performance tuningObjective #2: Evaluate triboelectric responses for metal ion detection and membrane regeneration actuationSubtask 2.1: Characterize the triboelectric response of sensor membranes for single/multiple metal solutions.Subtask 2.2: Develop CNN ML models for triboelectric membrane sensing to differentiate metalions for optimized membrane regeneration procedure.Objective #3: Evaluate the performance of the sensor membrane in hydroponic systemsSubtask 3.1: Grow leafy greens in a greenhouse using a nutrient film technology (NFT)hydroponics system.Subtask 3.2: Cultivate leafy greens in an indoor vertical farm using a deep-water culture (DWC)hydroponics system.