A plant-wide model describing GHG emissions and nutrient recovery options for water resource recovery facilities

Digitalization of phosphorous removal process in biological wastewater treatment systems: Challenges, and way forward

Phosphorus in wastewater poses a significant environmental threat, leading to water pollution and eutrophication. However, it plays a crucial role in the water-energy-resource recovery-environment (WERE) nexus. Recovering Phosphorus from wastewater can close the phosphorus loop, supporting circular economy principles by reusing it as fertilizer or in industrial applications. Despite the recognized importance of phosphorus recovery, there is a lack of analysis of the cyber-physical framework concerning the WERE nexus. Advanced methods like automatic control, optimal process technologies, artificial intelligence (AI), and life cycle assessment (LCA) have emerged to enhance wastewater treatment plants (WWTPs) operations focusing on improving effluent quality, energy efficiency, resource recovery, and reducing greenhouse gas (GHG) emissions. Providing insights into implementing modeling and simulation platforms, control, and optimization systems for Phosphorus recovery in WERE (P-WERE) in WWTPs is extremely important in WWTPs. This review highlights the valuable applications of AI algorithms, such as machine learning, deep learning, and explainable AI, for predicting phosphorus (P) dynamics in WWTPs. It emphasizes the importance of using AI to analyze microbial communities and optimize WWTPs for different various objectives. Additionally, it discusses the benefits of integrating mechanistic and data-driven models into plant-wide frameworks, which can enhance GHG simulation and enable simultaneous nitrogen (N) and Phosphorus (P) removal. The review underscores the significance of prioritizing recovery actions to redirect Phosphorus from effluent to reusable products for future considerations.

Machine learning assisted combined systems of wastewater treatment plants with constructed wetlands optimal decision-making

This study proposed an efficient framework for optimizing the design and operation of combined systems of wastewater treatment plants (WWTP) and constructed wetlands (CW). The framework coupled a WWTP model with a CW model and used a multi-objective evolutionary algorithm to identify trade-offs between energy consumption, effluent quality, and construction cost. Compared to traditional design and management approaches, the framework achieved a 27 % reduction in WWTP energy consumption or a 44 % reduction in CW cost while meeting strict effluent discharge limits for Chinese WWTP. The framework also identified feasible decision variable ranges and demonstrated the impact of different optimization strategies on system performance. Furthermore, the contributions of WWTP and CW in pollutant degradation were analyzed. Overall, the proposed framework offers a highly efficient and cost-effective solution for optimizing the design and operation of a combined WWTP and CW system.

Differences in greenhouse gas emissions and microbial communities between underground and conventionally constructed wastewater treatment plants

Large quantities of greenhouse gases (GHGs) are emitted into the atmosphere during wastewater treatment. In this study, GHG and microbial samples were collected from four wastewater treatment plants (WWTPs), and their differences and relationships were assessed. The study showed that, compared with conventionally constructed WWTPs, well-established gas collection systems in underground WWTPs facilitate comprehensive collection and accurate accounting of GHGs. In aboveground WWTPs, capped anoxic ponds promote methane production releasing it at 2-8 times the rate of uncapped emissions, in contrast to nitrous oxide emissions. Moreover, a stable subsurface environment allows for smaller fluctuations in daily GHG emissions and higher microbial diversity and abundance. This study highlights differences in GHG emission fluxes and microbial communities in differently constructed WWTPs, which are useful for control and accurate accounting of GHG emissions.

Net zero greenhouse emissions and energy recovery from food waste: manifestation from modelling a city-wide food waste management plan

Food waste (FW) being a major solid waste component and of degradable nature is the most challenging to manage and mitigate greenhouse gas emissions (GHEs). Policymakers seek innovative approaches to achieve net zero objectives and recover resources from the FW which requires a comparative and holistic investigation of contemporary treatment methods. This study assessed the lifecycle of six alternative scenarios for reducing net GHEs and energy use potential from FW management in a metropolis, taking Hong Kong as a reference. In both impact categories, the business-as-usual (landfilling) was the worst-case scenario. The combined anaerobic digestion and composting (ADC) technique was ranked best in the global warming impact but was more energy intensive than anaerobic digestion with sludge landfilling (ADL). Incineration ranked second in net GHEs but less favourable for energy recovery from FW alone. The proposed integration of FW and biological wastewater treatment represented an enticing alternative. Integration by co-disposal and treatment with wastewater (CoDT-WW) performed above average in both categories, while anaerobic co-digestion with sewage sludge (AnCoD-SS) ranked fourth. The sensitivity analysis further identified critical parameters inherent to individual scenarios along with biogenic carbon emission and sequestration, revealing their significance on the magnitude of GHEs and scenarios' ranking. Capacity assessment of the studied treatment facilities showed a FW diversion potential of ∼60% while reducing the net GHEs by ∼70% compared to the base-case, indicating potential of net zero carbon emissions and energy footprint by increasing treatment capacity. From this study, policymakers can gain insights and guidelines for low-carbon urban infrastructure development worldwide.

Automatic control and optimal operation for greenhouse gas mitigation in sustainable wastewater treatment plants: A review

In order to promote low-carbon sustainable operational management of the wastewater treatment plants (WWTPs), automatic control and optimal operation technologies, which devote to improving effluent quality, operational costs and greenhouse gas (GHG) emissions, have flourished in recent years. There is no consensus on the design procedure for optimal control/operation of sustainable WWTPs. In this review, we summarize recent researches on developing control and optimization strategies for GHG mitigation in WWTPs. Faced with the fact that direct carbon dioxide (CO₂) emissions (considered biological origin) are generally not included in the carbon footprint of WWTPs, direct emissions (nitrous oxide (N₂O), methane (CH₄)) and indirect emissions are paid much attention. Firstly, the plant-wide models with GHG dynamic simulation, which are employed to design and evaluate the automatic control schemes as well as representative studies on identifying key factors affecting GHG emissions or comprehensive performance are outlined. Then, both traditional and advanced control methods commonly used in GHG mitigation are reviewed in detail, followed by the multi-objective optimization practices of control/operational parameters. Based on the mentioned control and (or) optimization strategies, a novel design framework for the optimal control/operation of sustainable WWTPs is proposed. The findings and design framework proposed in the paper will provide guidance for GHG mitigation and sustainable operation in WWTPs. It is foreseeable that more accurate and appropriate plant-wide models together with flexible control methods and intelligent optimization strategies will be developed to satisfy the upgrading requirements of WWTPs in the future.

Techno-economic analysis of single-stage and temperature-phase anaerobic co-digestion of sewage sludge, wine vinasse, and poultry manure

Anaerobic co-digestion (AcoD) is a mature and consolidated waste management technology that can transform agro-industrial by-products into biogas and digestate. This study conducted a techno-economic assessment of bioenergy and agricultural fertilizer production from AcoD of sewage sludge, wine vinasse, and poultry manure. In this case study, three configurations were investigated: i) Scenario 1, AcoD in thermophilic temperature; ii) Scenario 2, AcoD in mesophilic temperature; and iii) Scenario 3, AcoD in a temperature phase (TPAD) system, where the digestate produced in the first reactor (thermophilic) feeds the second reactor (mesophilic). The process was designed to manage 24,022 m³ wine vinasse y^-1, 24,022 m³ sewage sludge y^-1, and 480 m³ poultry manure y^-1. The major cost was the fixed capital investment for the single-stage (320,981 USD) and TPAD processes (379,698 USD). The TPAD process produced the highest electricity (1058.99 MWh y^-1) and heat (4765.47 GJ y^-1) with the lowest cost of manufacturing for electricity (84.99 USD MWh^-1), heat (0.019 USD MJ^-1), and fertilizer (30.91 USD t^-1). Regarding the profitability indicators, the highest net present value (509,011 USD) and the lowest payback time (4.24 y) were achieved for Scenario 3. In conclusion, TPAD is a profitable and sustainable waste-to-energy management technology that can be applied in a circular economy framework to recover bioenergy and fertilizer, contributing to decreasing the carbon footprint of the agri-food sector.

Economic, technical, and environmental evaluation of retrofitting scenarios in a full-scale industrial wastewater treatment system

The use of mathematical models is a well-established procedure in the field of (waste) water engineering to "virtually" evaluate the feasibility of novel process modifications. In this way, only options with the highest chance of success are further developed to be implemented at full-scale, while less interesting proposals can be disregarded at an early stage. Nevertheless, there is still lack of studies, where different plant-wide model predictions (effluent quality, process economics, and technical aspects) are comprehensibly verified in the field with full-scale data. In this work, a set of analysis/evaluation tools are used to assess alternative retrofitting options in the largest industrial wastewater treatment plant in Northern Europe. A mechanistic mathematical model is simulated to reproduce process behavior (deviation < 11%). Multiple criteria are defined and verified with plant data (deviation < 5%). The feasibility of three types of scenarios is tested: (1) stream refluxing, (2) change of operational conditions and (3) the implementation of new technologies. Experimental measurements and computer simulations show that the current plant´s main revenues are obtained from the electricity produced by the biogas engine (54%) and sales of the inactivated bio-solids for off-site biogas production (33%). The main expenditures are the discharge fee (39%), and transportation and handling of bio-solids (30%). Selective treatment of bio-solid streams strongly modifies the fate of COD and N compounds within the plant. In addition, it increases revenues (+3%), reduces cost (-9%) and liberates capacity in both activated sludge (+25%) and inactivation reactors (+50%). Better management of the buffer tank promotes heterotrophic denitrification instead of dissimilatory nitrate conversion to ammonia. In this way, 11% of the incoming nitrogen is removed within the anaerobic water line and does not overload the activated sludge reactors. Only a marginal increase in process performance is achieved when the anaerobic granular sludge reactor operates at full capacity. The latter reveals that influent biodegradability is the main limiting factor rather than volume. Usage of either NaOH or heat (instead of CaO) as inactivation agents allows anaerobic treatment of the reject water, which substantially benefits revenues derived from higher electricity recovery (+44%). However, there is a high toll paid on chemicals (+73%) or heat recovery (-19%) depending on the inactivation technology. In addition, partial nitration/Anammox and a better poly-aluminum chloride (PAC) dosage strategy is necessary to achieve acceptable (< 2%) N and P levels in the effluent. The scenarios are evaluated from a sustainability angle by using life cycle impact assessment (LCIA) in form of damage stressors grouped into three categories: human health, ecosystems quality, and resource scarcity. The presented decision support tool has been used by the biotech company involved in the study to support decision-making on how to handle future expansions.