Machine Learning in Designing Sustainable Water Resource Recovery Facilities

Dear Colleagues,

The twelve design principles of sustainable environmental engineering attempt to transform how environmental engineering infrastructure systems can be better designed by prioritizing six hierarchies through six different dimensions. The six design hierarchies are prevention, recovery, separation, treatment, remediation, and optimization. The six dimensions are integrated system, material economy, reliability on spatial scale, resiliency on temporal scale, and cost effectiveness. Research or literature review papers focused on this theme and using machine learning (ML) and artificial intelligence (AI) are sought for this Special Issue. Specifically, CAPEX and OPEX in terms of dollar per kg removed COD will be quantified in innovative technologies. The innovative technologies include VUV disinfection, AnMBR, Anammox, granular activated sludge, MBBR, fuel cells for the electrical generation for water resource recovery facilities (WRRFs). WRRFs will be assessed according to different flow rates under different population equivalence (PE) retrofitting, new design, wastewater reuse, or retrofitting WRRF.

1) Introduction

Water resource recovery facility is a new design paradigm of future wastewater treatment plants. It aims toward the reuse of treated effluent, self-sufficient energy, nutrient recovery, and beneficial uses of biosolids. Recently, there has been exponential growth in research related to the applications of machine learning (ML), deep learning (DL), and artificial intelligence (AI) in these areas. Therefore, this Special Issue will present advances in these areas.

2) Aim

Original papers and literature reviews of the latest developments in designing modular and standardized reactors of innovative WRRF through ML, DL, and AI are welcome.  The aim is to lay a foundation for large databases and develop future hidden layers of algorithms for the twelve design principles of SEE.

3) Suggest themes

In this Special Issue, original research articles and reviews on “Machine Learning in Designing Sustainable Water Resource Recovery Facilities” are welcome. Research areas may include (but not limited to) the following:

• CAPEX and OPEX of WRRF processes and systems
• Innovative technology of WRRF
• Enhanced coagulation for microsieving
• Anaerobic MBR
• Moving bed biological reactor
• VUV disinfection
• Anammox
• Granular activated sludge
• Hydrogen production of fuel cell
• Unit energy production per COD removed of each WRRF innovative technologies

I look forward to receiving your contributions.

Dr. Walter Z. Tang
Guest Editor

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• VUV disinfection
• AnMBR
• Anammox
• granular activated sludge
• MBBR
• fuel cells
• water resource recovery facility