Quantitative evaluation of A2O and reversed A2O processes for biological municipal wastewater treatment using a projection pursuit method

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.

Calculation and analysis of agricultural carbon emission efficiency considering water-energy-food pressure: Modeling and application

Agricultural carbon emission efficiency (ACEE) measurement, a tool for effectively achieving sustainable development goals, has garnered much attention. However, the impacts of resource pressures such as water, energy and food on ACEE have been overlooked, and the high dimensionality of the measurement model and insufficient sample data can easily distort the measurement results. Therefore, from a green development perspective, we established a new ACEE measurement framework considering the water-energy-food pressure index and a new integrated ACEE measurement model (CSMA-PPE-USSBM) that includes chaotic maps, the slime mould algorithm (SMA), projection pursuit evaluation (PPE) and the undesirable super slack-based measure (USSBM). The model was used to calculate ACEE in 13 prefecture-level municipalities in Heilongjiang Province, China, and analyze its spatiotemporal evolution and possible causes. The results showed that this model avoids the above problems. The reliability coefficient and stability coefficient reached 0.962 and 0.971, respectively; ACEE in Heilongjiang Province has much room for improvement, but there are obvious differences in carbon emission efficiency in different carbon emission type regions. The key driving forces of ACEE variation can generate significant scale effects. Provincial driving factors can affect ACEE variation in prefecture municipalities, where the influence range is limited or the influence of driving factors gradually emerges. The research results provide a theoretical reference for accurately measuring the regional ACEE and analyzing the driving mechanism of ACEE and green agricultural development.

Full-scale application and performance of a new multi-self-reflow decentralized Wastewater treatment device: Impact of hydraulic and pollutant loads

Decentralized treatment of wastewater in rural areas usually has several challenges, which include large fluctuations in pollutant concentration and water quantity, complicated operation and maintenance of conventional biochemical treatment equipment, resulting in poor stability and a low compliance rate of the wastewater treatment process. In order to solve the above problems, a new integration reactor is designed, which uses gravity and aeration tail gas self-reflux technology to realize the reflux of sludge and the nitrification liquid, respectively. The feasibility and operation characteristics of its application for decentralized wastewater treatment in rural areas are explored. The results demonstrated that, under constant influent, the device showed strong tolerance to the shock of pollutant load. The chemical oxygen demand, NH₄⁺-N, total nitrogen and total phosphorus fluctuated in the ranges of 95-715 mg/L, 7.6-38.5 mg/L, 9.32-40.3 mg/L and 0.84-4.9 mg/L, respectively. The corresponding effluent compliance rates were 82.1%, 92.8%, 96.4% and 96.3%, respectively. When the wastewater discharge was non-constant and the maximum single-day Q_max/Q_min reached 5, all indicators of the effluent met the relevant discharge standard. The integrated device also demonstrated high phosphorus enrichment levels in its anaerobic zone; the concentration of phosphorus reached a maximum of 26.9 mg/L, which created a good environment for phosphorus removal. The microbial community analysis showed that sludge digestion, denitrification, and phosphorus-accumulating bacteria all played an important role in pollutant treatment.

Identification of the interference spectra of edible oil samples based on neighborhood rough set attribute reduction

Due to numerous edible oil safety problems in China, an automatic oil quality detection technique is urgently needed. In this study, rough set theory and Fourier transform spectrum are combined for proposing a digital identification method for edible oil. First, the Fourier transform spectra of three different types of edible oil samples, including colza oil, waste oil, and peanut oil, are measured. After the input spectra are differentially and smoothly processed, the characteristic wavelength bands are selected with neighborhood rough set attribution reduction (NRSAR). Moreover, the classification models are established based on random forest (RF) and extreme learning machine (ELM) algorithms. Finally, confusion matrix, classification accuracy, sensitivity, specificity, and the distribution of judgment are calculated for evaluating the classification performances of different models and determining the optimal oil identification model. The results show that by using the third-order difference pre-processing method, 193 wavelength bands in the visible range can be reduced to 10 characteristic wavelengths, with a compression ratio of over 88.61%. Using the established NRS-RF and NRS-ELM models, the total identification accuracies are 91.67% and 93.33%, respectively. In particular, the identification accuracy of peanut oil using the NRS-ELM model reaches up to 100%, whereas the identification accuracies obtained using the principal component analysis (PCA)-based models that are commonly used in information processing (PCA-RF and PCA-ELM) are 81.67% and 90.00%, respectively. As compared with feature extraction methods, the proposed NRSAR shows directive advantages in terms of precision, sensitivity, specificity, and the distribution of judgment. In addition, the execution time is also reduced by approximately 1/3. Conclusively, the NRSAR method and NRS-ELM the model in the spectral identification of edible oil show favorable performance. They are expected to bring forth insightful oil identification techniques.

Effect of aniline and antimony on anaerobic-anoxic-oxic system with novel amidoxime-modified polyacrylonitrile adsorbent for wastewater treatment

There has been increasing concern over the mixed discharge of municipal-textile composite wastewater, which remains challenging for typical wastewater treatment plant (WWTP) using anaerobic-anoxic-oxic process (AAO). Highly-toxic aniline and antimony, typical co-contaminants in textile wastewater, usually lead to increased chemical oxygen demand (COD) in influent and deteriorated effluent quality. Amidoxime-modified polyacrylonitrile (amPAN) adsorbent was prepared and added to adsorb antimony and facilitate substrate removal. With amPAN dosage at 6.0 g L^-1 in oxic bioreactor, 64.2 ± 5.6% of antimony was removed from influent. Extracellular polymeric substance release was simultaneously changed with residual antimony concentration. Meanwhile, amPAN promoted the proliferation of Proteobacteria, Bacteroidetes and Epsilonbacteraeota serving as microorganism carrier. As a result, removal efficiencies of COD (94.4 ± 0.6%), ammonium (NH₄⁺-N, 92.6 ± 3.3%), total nitrogen (TN, 76.4 ± 6.3%) and total phosphorus (TP, 93.4 ± 2.1%) were enhanced to meet Class 1A discharge standard in China. These results indicate that AAO with amPAN is promising for municipal-textile composite wastewater treatment.

Cost coupled removal efficiency analyses of activated sludge technologies to achieve the cost-effective wastewater treatment system in the meat processing units

This study was conducted to distinguish the most cost-effective activated sludge-based wastewater treatment technology by focusing on removal efficiency (RE) for applying in the meat processing units. Four different wastewater treatment plants (WWTPs), namely anaerobic/anoxic/oxic (A₂O), membrane bioreactor (MBR), moving bed bioreactor (MBBR), and integrated fixed-film activated sludge (IFAS) were simulated regarding the daily annual data obtained from a meat processing unit. The descending order concerning the total cost (TC) was as follows: MBR > A₂O > IFAS > MBBR while it was as MBR > IFAS > MBBR > A₂O based on RE. Regarding the energy and material consumption costs, the A₂O and MBR were the highest and lowest cost-effective systems, respectively. However, the MBR was the best from a biological treatment cost perspective. The boilers and co-generators contributed to 75% and 25% of the produced biogas, respectively, that supplied 50% of WWTP electricity demand. Among all different influent parameters, the effect of flow rate, COD, and BOD on the TC was statistically significant. Furthermore, the TC and present worth illustrated the most sensitivity to the construction cost alteration. This research provides insights into key economic parameters for a WWTP design and application.

Iron-carbon galvanic cells strengthened anaerobic/anoxic/oxic process (Fe/C-A2O) for high-nitrogen/phosphorus and low-carbon sewage treatment

The treatment of sewage with high-nitrogen/-phosphorus and low-carbon remains a challenge. A novel iron-carbon galvanic cells strengthened anaerobic/anoxic/oxic process (Fe/C-A2O) was developed for high-nitrogen/-phosphorus and low-carbon sewage treatment. The cost-effective iron-scraps (ISs) was recycled as Fe(0)-source under the mediation of Fe/C galvanic cell reaction to develop effective Fe(0)-oxidizing autotrophic-denitrification and -dephosphorization. Utilizing practical high-nitrogen/-phosphorus and low-carbon sewage as target wastewater, the performance, impact factors, contribution of Fe/C galvanic cell reactions, microbial characteristics, strengthening mechanisms, and application potential of Fe/C-A2O process were investigated. The Fe/C-A2O process achieved high TN and TP removal efficiencies of 92.0 ± 1.3% and 97.2 ± 0.9% with removal loads of 0.176 ± 0.002 kg TN/(m³·d) and 0.017 ± 0.002 kg TP/(m³·d), respectively. Optimal HRT of 12 h, DO of 4.0-4.5 mg/L, and reflux-ratio of 4:1 were obtained, and no sludge-reflux was required. Autotrophic-denitrification and -dephosphorization supported by the Fe/C galvanic cell reactions contributed 63.1% and 75.3% of TN and TP removal, respectively. Microbial characterization revealed the dominance of autotrophic denitrifiers (e.g., Thiobacillus), AOB (e.g., Nitrosomonas), NOB (e.g., Nitrospira), and heterotrophic denitrifiers (e.g., Zoogloea). The mechanism analysis demonstrated that Fe/C galvanic cells strengthened nitrogen removal by raising Fe²⁺/H₂-supported autotrophic denitrification; and strengthened dephosphorization by introducing Fe³⁺-based PO₄^3--precipitation and enhancing the denitrifying phosphate-accumulation by denitrifying phosphate-accumulating organisms (DPAOs). Based on the efficiency and cost evaluation, the ISs-based Fe/C-A2O process showed significant application potential as an upgrade strategy for traditional A2O process in advanced high-nitrogen/phosphorus and low-carbon sewage treatment.

An influent responsive control strategy with machine learning: Q-learning based optimization method for a biological phosphorus removal system

Biological phosphorus removal (BPR) is an economical and sustainable processes for the removal of phosphorus (P) from wastewater, achieved by recirculating activated sludge through anaerobic and aerobic (An/Ae) processes. However, few studies have systematically analyzed the optimal hydraulic retention times (HRTs) in anaerobic and aerobic reactions, or whether these are the most appropriate control strategies. In this study, a novel optimization methodology using an improved Q-learning (QL) algorithm was developed, to optimize An/Ae HRTs in a BPR system. A framework for QL-based BPR control strategies was established and the improved Q function, Q^t+1(s_t,s_t+1)=Q^t(s_t,s_t+1)+k·[R(s_t,s_t+1)+γ·maxa^tQ^t(s_t,s_t+1)-Q^t(s_t,s_t+1)] was derived. Based on the improved Q function and the state transition matrices obtained under different HRT step-lengths, the optimum combinations of HRTs in An/Ae processes in any BPR system could be obtained, in terms of the ordered pair combinations of the <current state-transition state>. Model verification was performed by applying six different influent chemical oxygen demand (COD) concentrations, varying from 150 to 600 mg L^-1 and influent P concentrations, varying from 12 to 30 mg L^-1. Superior and stable effluent qualities were observed with the optimal control strategies. This indicates that the proposed novel QL-based BPR model performed properly and the derived Q functions successfully realized real-time modelling, with stable optimal control strategies under fluctuant influent loads during wastewater treatment processes.

Intelligent Control/Operational Strategies in WWTPs through an Integrated Q-Learning Algorithm with ASM2d-Guided Reward

The operation of a wastewater treatment plant (WWTP) is a typical complex control problem, with nonlinear dynamics and coupling effects among the variables, which renders the implementation of real-time optimal control an enormous challenge. In this study, a Q-learning algorithm with activated sludge model No. 2d-guided (ASM2d-guided) reward setting (an integrated ASM2d-QL algorithm) is proposed, and the widely applied anaerobic-anoxic-oxic (AAO) system is chosen as the research paradigm. The integrated ASM2d-QL algorithms equipped with a self-learning mechanism are derived for optimizing the control strategies (hydraulic retention time (HRT) and internal recycling ratio (IRR)) of the AAO system. To optimize the control strategies of the AAO system under varying influent loads, Q matrixes were built for both HRTs and IRR optimization through the pair of <max reward-action> based on the integrated ASM2d-QL algorithm. 8 days of actual influent qualities of a certain municipal AAO wastewater treatment plant in June were arbitrarily chosen as the influent concentrations for model verification. Good agreement between the values of the model simulations and experimental results indicated that this proposed integrated ASM2d-QL algorithm performed properly and successfully realized intelligent modeling and stable optimal control strategies under fluctuating influent loads during wastewater treatment.

A New Model of Projection Pursuit Grade Evaluation Model Based on Simulated Annealing Ant Colony Optimization Algorithm

Projection pursuit model (PP) is widely used in many fields, especially quality evaluation. One of the biggest shortages of PP was that the projection direction is strongly influenced by relevant parameters. In order to solve this problem, many experts and scholars introduced all kinds of parameters optimization method in PP. Based on the basis of previous studies, the article proposed a new model of projection pursuit grade evaluation model (PPE) integrated with simulated annealing ant colony optimization algorithm (SA-ACO). It provided a new thought and method for quality evaluation research. The case example demonstrated that the accuracy and the effect evaluation of the model was effectively and more objectively and practical in the evaluation of quality.

A modeling understanding on the phosphorous removal performances of A2O and reversed A2O processes in a full-scale wastewater treatment plant

Reversed A²O process (anoxic-anaerobic-aerobic) and conventional A²O process (anaerobic-anoxic-aerobic) are widely used in many wastewater treatment plants (WWTPs) in Asia. However, at present, there are still no consistent results to figure out which process has better total phosphorous (TP) removal performance and the mechanism for this difference was not clear yet. In this study, the treatment performances of both processes were compared in the same full-scale WWTP and the TP removal dynamics was analyzed by a modeling method. The treatment performance of full-scale WWTP showed the TP removal efficiency of the reversed A²O process was more efficient than in the conventional A²O process. The modeling results further reveal that the TP removal depends highly on the concentration and composition of influent COD. It had more efficient TP removal than the conventional A²O process only under conditions of sufficient influent COD and high fermentation products content. This study may lay a foundation for appropriate selection and optimization of treatment processes to suit practical wastewater properties.

Reductions of dissolved organic matter and disinfection by-product precursors in full-scale wastewater treatment plants in winter

The reductions of dissolved organic matter (DOM) and disinfection byproduct precursors in four full-scale wastewater treatment plants (WWTPs) (Liaoning Province, China) where different biological treatment processes were employed in winter were investigated. The total removal efficiencies of dissolved organic carbon (DOC), ultraviolet light at 254 nm (UV-254), trihalomethane formation potential (THMFP), and haloacetic acid formation potential (HAAFP) were in the range of 70.3-76.0%, 49.6-57.3%, 54.4-65.0%, and 53.7-63.8% in the four WWTPs, respectively. The biological treatment was the predominant process responsible for the removal of DOC, THMFP, and HAAFP in WWTPs. Differences in the reduction of UV-254 were not significant (p > 0.05) among biochemical reaction pool, secondary sedimentation tank, and disinfection tank. Biological aerated filter and suspended carrier activated sludge processes achieved higher DOM removal than the conventional active sludge and anaerobic-anoxic-oxic processes. Hydrophobic neutral and hydrophilic fraction were removed to a higher degree through biological treatment than the other three DOM fractions. HAAFP removal was more efficient than THMFP reduction during biological treatment. During primary treatment, fluorescent materials in secondary sedimentation tanks were preferentially removed, as compared to the bulk DOM. Humic-like fluorescent compounds were not readily eliminated during biological treatment. The fluorescent materials were more susceptible to chlorine than nonfluorescent compounds.