Evaluation of chemical sludge production in wastewater treatment processes

Guimarães, Natália Rodrigues; Filho, Sidney Seckler Ferreira; Hespanhol, Bruno Piotto; Piveli, Roque Passos

doi:10.1080/19443994.2015.1081624

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…EBPR-A had higher TP removal efficiency compared with basic EBPR, showing the positive impact of maintaining a C/P ratio between 26 and 30 to avoid PAO carbon limitations . EBPR-S had the highest removal efficiency and narrowest effluent TP range as it dramatically reduced the amount of P returning from solids digestion and thickening operations to mainline treatment, alleviating carbon limitations and reducing sludge production (Figure c) …”

Section: Resultsmentioning

confidence: 99%

“…36 EBPR-S had the highest removal efficiency and narrowest effluent TP range as it dramatically reduced the amount of P returning from solids digestion and thickening operations to mainline treatment, alleviating carbon limitations and reducing sludge production 2c). 37 Nitrification and partial denitrification in the AS process led to a reduction in effluent TN concentration. This reduction was slightly improved in the ASCP process, due to increased sludge settleability from chemical addition.…”

Section: Resultsmentioning

confidence: 99%

“…While iron precipitation resulted in the lowest effluent P concentrations, it significantly contributed to sludge production (Figure 2e). Chemical sludge production is reported to increase linearly with coagulant dosage, 37 while extra sludge production in an EBPR process is expected to be between 30 to 60% of that obtained in a chemical process. 38 3.1.2.…”

Section: Resultsmentioning

confidence: 99%

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Evaluating Long-Term Treatment Performance and Cost of Nutrient Removal at Water Resource Recovery Facilities under Stochastic Influent Characteristics Using Artificial Neural Networks as Surrogates for Plantwide Modeling

Emaminejad

Aguiar

et al. 2021

ACS EST Eng.

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Integrated watershed modeling is needed to couple water resource recovery facilities (WRRFs) with agricultural management for holistic watershed nutrient management. Surrogate modeling can facilitate model coupling. This study applies artificial neural networks (ANNs) as surrogate models for WRRF models to efficiently evaluate the long-term treatment performance and cost under influent fluctuations. Specifically, we first developed five WRRFs, including activated sludge, activated sludge with chemical precipitation (ASCP), enhanced biological phosphorus removal (EBPR), EBPR with acetate addition (EBPR-A), and EBPR with struvite recovery (EBPR-S), in a high-fidelity simulation program (GPS-X). The five WRRFs were based on an existing plant that treats combined domestic and industrial wastewater. The ANNs have satisfactory performance in capturing nonlinear biological behaviors for all five WRRFs, even though the prediction performance (R-square) slightly decreases as the model complexity increases. We advanced ANNs application in WRRF models by simulating long-term (10-yr) performance with monthly influent fluctuations using ANNs trained by simulation data from steady-state models and evaluated their performance on Phosphorus (P) and Nitrogen (N) removal. EBPR-S shows the most resilience, while EBPR is more sensitive to influent characteristics impacted by stormwater inflow. When comparing life cycle costs of N and P removal for each layout over the 10-yr simulation period, EPBR-S is the most cost-effective alternative, highlighting both the operational and cost benefits of side-stream P recovery. By capturing both nonlinear behaviors of biological treatment and operating costs with computationally lean ANNs, this study provides a paradigm for integrating complex WRRF models within integrated watershed modeling frameworks.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Evaluating Long-Term Treatment Performance and Cost of Nutrient Removal at Water Resource Recovery Facilities under Stochastic Influent Characteristics Using Artificial Neural Networks as Surrogates for Plantwide Modeling

Emaminejad

Aguiar

et al. 2021

ACS EST Eng.

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“…Traditionally, wastewater from various industrial sectors is treated using chemical (disinfection, flocculation, neutralization, and oxidation) and/or physical (floatation, grit chamber, and screening) methods [7]. Chemical/physical treatments remain expensive and generate significant quantities of slurry/sludge, which requires a secondary treatment [8]. Wastewater treatment processes consume a lot of energy (2-4% of total national electric power), and need skilled workers to operate the treatment plants which, in turn, have a high capital cost for infrastructures [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Integrated Approach for Wastewater Treatment and Biofuel Production in Microalgae Biorefineries

et al. 2021

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The increasing world population generates huge amounts of wastewater as well as large energy demand. Additionally, fossil fuel’s combustion for energy production causes the emission of greenhouse gases (GHG) and other pollutants. Therefore, there is a strong need to find alternative green approaches for wastewater treatment and energy production. Microalgae biorefineries could represent an effective strategy to mitigate the above problems. Microalgae biorefineries are a sustainable alternative to conventional wastewater treatment processes, as they potentially allow wastewater to be treated at lower costs and with lower energy consumption. Furthermore, they provide an effective means to recover valuable compounds for biofuel production or other applications. This review focuses on the current scenario and future prospects of microalgae biorefineries aimed at combining wastewater treatment with biofuel production. First, the different microalgal cultivation systems are examined, and their main characteristics and limitations are discussed. Then, the technologies available for converting the biomass produced during wastewater treatment into biofuel are critically analyzed. Finally, current challenges and research directions for biofuel production and wastewater treatment through this approach are outlined.

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“…methods. These methods are all expensive and result in sludge production and secondary water pollution [9]. Water reservoirs are becoming more contaminated as a result of rising levels of micropollutants such as medicines, organic polymers, and suspended particles.…”

Section: Introductionmentioning

confidence: 99%

Recent Developments in Microbial Electrolysis Cell-Based Biohydrogen Production Utilizing Wastewater as a Feedstock

Dange

Pandit

Jadhav³

et al. 2021

Sustainability

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Carbon constraints, as well as the growing hazard of greenhouse gas emissions, have accelerated research into all possible renewable energy and fuel sources. Microbial electrolysis cells (MECs), a novel technology able to convert soluble organic matter into energy such as hydrogen gas, represent the most recent breakthrough. While research into energy recovery from wastewater using microbial electrolysis cells is fascinating and a carbon-neutral technology that is still mostly limited to lab-scale applications, much more work on improving the function of microbial electrolysis cells would be required to expand their use in many of these applications. The present limiting issues for effective scaling up of the manufacturing process include the high manufacturing costs of microbial electrolysis cells, their high internal resistance and methanogenesis, and membrane/cathode biofouling. This paper examines the evolution of microbial electrolysis cell technology in terms of hydrogen yield, operational aspects that impact total hydrogen output in optimization studies, and important information on the efficiency of the processes. Moreover, life-cycle assessment of MEC technology in comparison to other technologies has been discussed. According to the results, MEC is at technology readiness level (TRL) 5, which means that it is ready for industrial development, and, according to the techno-economics, it may be commercialized soon due to its carbon-neutral qualities.

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Evaluation of chemical sludge production in wastewater treatment processes

Cited by 10 publications

References 27 publications

Evaluating Long-Term Treatment Performance and Cost of Nutrient Removal at Water Resource Recovery Facilities under Stochastic Influent Characteristics Using Artificial Neural Networks as Surrogates for Plantwide Modeling

Evaluating Long-Term Treatment Performance and Cost of Nutrient Removal at Water Resource Recovery Facilities under Stochastic Influent Characteristics Using Artificial Neural Networks as Surrogates for Plantwide Modeling

Integrated Approach for Wastewater Treatment and Biofuel Production in Microalgae Biorefineries

Recent Developments in Microbial Electrolysis Cell-Based Biohydrogen Production Utilizing Wastewater as a Feedstock

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