Carbon neutrality in wastewater treatment plants: An integrated biotechnological-based solution for nutrients recovery, odour abatement and CO2 conversion in alternative energy drivers

Chemosphere, Journal Year: 2024, Volume and Issue: 354, P. 141700 - 141700

Published: March 13, 2024

Wastewater treatment plants play a crucial role in water security and sanitation, ensuring ecosystems balance avoiding significant negative effects on humans environment. However, they determine also pressures, including greenhouse gas odourous emissions, which should be minimized to mitigate climate changes besides complaints. The research has been focused the validation of an innovative integrated biological system for sustainable complex gaseous emissions from wastewater plants. proposed consists moving bed biofilm reactor coupled with algal photobioreactor, dual objective of: i) reducing inlet concentration contaminants (in this case, hydrogen sulphide, toluene p-xylene); ii) capturing converting carbon dioxide produced by degradation process into exploitable biomass. first promoted chemical compounds up 99.57% load (IL) 22.97 g m−3 d−1 while second allowed capture CO2 resulting compounds, biofixation rate 81.55%. absorbed was converted valuable feedstocks, maximum biomass productivity aPBR 0.22 L−1 d−1. Dairy used as alternative nutrient source both reactors, view reusing cultivating biomass, framing technology context biorefinery within circular economy perspective. photobioreactor indeed characterized high lipid content, percentage lipids per dry weight 35%. can therefore exploited production clean energy carrier. biotechnology represents effective tool shifiting conventional neutral platform implementing principles ecological transition achieving levels environmental protection.

Language: Английский

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Transformative Intelligence

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 323 - 338

Published: March 7, 2025

This chapter explores the pivotal role of Artificial Intelligence in revolutionizing wastewater treatment. Fuzzy Logic Controllers adeptly handle incomplete data, and Neural Networks (ANNs) model intricate processes, while Neuro-Fuzzy Systems seamlessly integrate fuzzy logic ANNs for superior water disinfection control. State observers elevate concentration estimation accuracy, metaheuristic algorithms such as PSO ABC optimize control processes. AI-driven Fault Detection Isolation enhance safety operations. Beyond control, AI shapes intelligent management smart cities energy-saving strategies, providing a versatile framework tackling complexities. In conclusion, emphasizes significance algorithms, highlighting their adaptability efficiency transformative landscape integration not only ensures operational efficacy but also sets stage sustainable environmental management, it is enhanced with internet things.

Language: Английский

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Effective evaluation of greenhouse gases (GHGs) emissions from anoxic/oxic (A/O) process of regenerated papermaking wastewater treatment through hybrid deep learning techniques: Leveraging the critical role of water quality indicators

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 380, P. 125094 - 125094

Published: April 1, 2025

Language: Английский

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Development and application of an intelligent nitrogen removal diagnosis and optimization framework for WWTPs: Low-carbon and stable operation

Water Research, Journal Year: 2024, Volume and Issue: 266, P. 122337 - 122337

Published: Aug. 30, 2024

Language: Английский

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A binary-level hybrid intelligent control configuration for sustainable energy consumption in an activated sludge biological wastewater treatment plant

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 65, P. 105902 - 105902

Published: Aug. 1, 2024

Language: Английский

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Improvement of effluent quality and operational cost reduction of a wastewater treatment plant using intelligent computational hybrid control schemes

Clean Technologies and Environmental Policy, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 27, 2024

Language: Английский

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Wastewater Treatment Process Enhancement Based on Multi-Objective Optimization and Interpretable Machine Learning

Published: Jan. 1, 2024

Optimization and control of wastewater treatment process (WTP) can contribute to cost reduction efficiency. A multi-objective optimization (MOO) framework is proposed in this paper provide suggestions for decision-making setting parameters WTP. Firstly, the prediction models based on Extreme Gradient Boosting (XGB) with Bayesian (BO) are developed predicting effluent water quality (EQ) energy consumption (EC) different influent parameter settings. Then, SHapley Additive exPlanations (SHAP) algorithm used complement interpretability machine learning quantitatively evaluate impact features predicted targets. Lastly, Non-dominated Sorting Genetic Algorithm II (NSGA-II) introduced solve MOO problem final decision solution obtained Technique Order Preference by Similarity Ideal Solution (TOPSIS) method. To illustrate applicability framework, above methods applied Benchmark simulation model No. 1 (BSM1). Results indicate that BOXGB effectively capture complex nonlinear relationship WTP R2 values 0.923 0.965 EQ EC, respectively. Additionally, NSGA-II TOPSIS efficiently determine ideal operating parameters. demonstrate enables while ensuring various scenarios.

Language: Английский

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Seasonal Setpoints Optimization of WWTP DO Control Based on Artificial Neural Networks Performance Indices Prediction

Computer-aided chemical engineering/Computer aided chemical engineering, Journal Year: 2024, Volume and Issue: unknown, P. 1615 - 1620

Published: Jan. 1, 2024

Language: Английский

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Enerji Sistemlerinde Metasezgisel Optimizasyon Teknikleri: Yenilikçi Algoritmalar ve Uygulama Alanları

Sürdürülebilir Mühendislik Uygulamaları ve Teknolojik Gelişmeler Dergisi, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 25, 2024

Optimizasyon, tüm olası alternatifler arasından bir problemin en optimal çözümünü belirleme sürecidir. Enerji sistemlerinde metasezgisel optimizasyon algoritmaları, karmaşık enerji problemlerini çözmede önemli rol oynamaktadır. Metasezgisel genetik algoritmalar, parçacık sürü optimizasyonu, simüle edilen tavlama, karınca kolonisi optimizasyonu gibi doğal süreçlerden esinlenerek geliştirilen ve genellikle bilgisayar tabanlı modellerle kullanılan özel yöntemleridir. büyük veri setleriyle çalışabilir farklı kısıtlamalar altında optimize edilmesi gereken çok sayıda değişkeni ele alabilirler. Bu nedenle sektöründe sürdürülebilirlik, verimlilik karlılık açısından öneme sahiptirler. verimliliğini artırmak, maliyetini azaltmak, üretimi, dağıtımı, tüketimi depolanması sistemlerinin bileşenlerini etmek için, yenilenebilir kaynaklarını entegre karbon ayak izini azaltmak çeşitli hedeflere ulaşmak için kullanılmaktadırlar. çalışmada, sistemleri uygulamalarında algoritmalarının kullanımı örnekler üzerinden incelenmiştir. algoritmaların ile problemlerin çözümlerinin daha kolaya indirgendiği görülmüştür.

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Short-term memory artificial neural network modelling to predict concrete corrosion in wastewater treatment plant inlet chambers using sulphide sensors

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 69, P. 106821 - 106821

Published: Dec. 26, 2024

Language: Английский

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