Xu Gao

The sewage treatment systems manage to reduce pollutants of wastewater to make it reach some requirement. The core of it is to effectively control and determine intermediate aeration amount, which is always challenging. Therefore, a multi‐objective planning mechanism (multi‐objective optimization design combining GRA and fuzzy logic inference) that combines grey relational analysis and fuzzy logic, to find the optimal dissolved oxygen solubility for achieving better outlet water quality, is proposed. First of all, grey correlation coefficient between each optimization target and the reference target is calculated, and are converted into fuzzy inference values through the four steps. After that, it is expected to analyse the average values of process variables to obtain the optimal parameter combinations. A real‐world dataset collected from a realistic sewage treatment plant is utilized as the simulation environment to evaluate the proposed multi‐objective optimization design combining GRA and fuzzy logic inference. Experimental results show that the multi‐objective optimization design combining GRA and fuzzy logic inference makes promotion of 47.34% for fitted outlet water quality compared with the original average annual water quality.

Although the management and planning of water resources are extremely significant to human development, the complexity of implementation is unimaginable. To achieve this, the high-precision water consumption prediction is actually the key component of urban water optimization management system. Water consumption is usually affected by many factors, such as weather, economy, and water prices. If these impact factors are directly combined to predict water consumption, the weight of each perspective on the water consumption will be ignored, which will be greatly detrimental to the prediction accuracy. Therefore, this paper proposes a deep neural network-based complex system for urban water management. The essence of it is to formulate a water consumption prediction model with the aid of principal component analysis (PCA) and the integrated deep neural network, which is abbreviated as UWM-Id. The PCA classifies the factors affecting water consumption in the original data into three categories according to their correlation and inputs them into the neural network model. The results in the previous step are assigned weights and integrated into the form of fully connected layer. Finally, analyzing the sensitivity of the proposed UWM-Id and comparing its performance with a series of commonly used baseline methods for data mining, a large number of experiments have proved that UWM-Id has good performance and can be used for urban water management system.

As a new wastewater biological nitrogen removal process, anammox and partial denitrification coupling not only plays a significant role in the nitrogen cycle, but also holds high engineering application value. Because anammox and some denitrifying bacteria are coupled under harsh living conditions, certain operating conditions and mechanisms of the coupling process are not clear; thus, it is more difficult to control the process, which is why the process has not been widely applied. This paper analyzes the research focusing on the coupling process in recent years, including anammox and partial denitrification coupling process inhibitors such as nitrogen (NH₄ ⁺, NO₂ ⁻), organics (toxic and non-toxic organics), and salts. The mechanism of substrate removal in anammox and partial denitrification coupling nitrogen removal is described in detail. Due to the differences in process methods, experimental conditions, and sludge choices between the rapid start-up and stable operation stages of the reactor, there are significant differences in substrate inhibition. Multiple process parameters (such as pH, temperature, dissolved oxygen, redox potential, carbon-to-nitrogen ratio, and sludge) can be adjusted to improve the coupling of anammox and partial denitrification to modify nitrogen removal performance.

The effects of formic acid, acetic acid and levulinic acid on acetone–butanol–ethanol (ABE) fermentation under different pH adjustment conditions were investigated using Clostridium acetobutylicum as the fermentation strain. CaCO₃ supplementation can alleviate the inhibitory effect of formic acid on ABE production. The ABE titers from the medium containing 0.5 g L⁻¹ formic acid with pH adjusted by CaCO₃ and KOH were 11.08 g L⁻¹ and 1.04 g L⁻¹, which reached 64.8% and 6.3% of the control group, respectively. Compared with CaCO₃ pH adjustment, fermentation results with higher ABE titers and yields were obtained from the medium containing acetic acid or levulinic acid, when the pH was adjusted by KOH. When formic acid, acetic acid, and levulinic acid co-existed in the medium, better fermentation result was achieved by adjusting the pH by CaCO₃. Moreover, 12.50 g L⁻¹ ABE was obtained from the medium containing 2.0 g L⁻¹ acetic acid, 0.4 g L⁻¹ formic acid, and 1.0 g L⁻¹ levulinic acid as compared to 3.98 g L⁻¹ ABE obtained from the same medium when the pH was adjusted by KOH. CaCO₃ supplementation is a more favorable pH adjustment method for ABE medium preparation from lignocellulosic hydrolysate.

Utilization-associated products (UAPs) and biomass-associated products (BAPs) were quantified separately in this study to characterize soluble microbial products (SMPs) in a drinking water lab-scale biological aerated filter (BAF), and their basic characteristics were explored using gel filtration chromatography and three-dimensional excitation–emission matrix (3D-EEM) spectrophotometry with fluorescence regional integration analysis and parallel factor model. UAPs were observed increased with the increase of filter media depth and accumulated after BAF treatment, whereas BAPs were basically constant. 3D-EEM spectroscopy analysis result showed that tryptophan and protein-like compounds were the main components of UAPs and BAPs, and fulvic-acid-like substance was a major component of BAPs, rather than UAPs. In terms of molecular weight (MW) distribution, UAP MW presented a bimodal distribution in the range of 1–5 and >10 kDa, while BAP MW exhibited unimodal distribution with MW >20 kDa fraction accounting for more than 90 %. The macromolecules of UAPs accumulated after BAF treatment. This study provides theoretical support for in-depth study of SMP characteristics.

Peak identification, FRI and PARAFAC were adopted and compared to analyze the 3D-EEM spectra.

The occurrence of 21 selected pharmaceutically active compounds (PhACs) in river water was examined; meanwhile the presence and removal of these PhACs at different water treatment steps in two conventional drinking water treatment plants (DWTPs) were evaluated. Monitoring results showed that of all 21 target PhACs, twelve PhACs were ubiquitously detected in water samples from the Yangtze river and its two tributaries (Jialing River and Huaxi stream), whereas alodipine was not found in any river water sample. The concentrations of PhACs detected in Huaxi stream were statistically significantly higher than those found in the Jialing River and the Yangzi River because of the direct discharges of uncontrolled domestic sewage and poultry wastewater and the abuse of PhACs in the rural areas served by the Huaxi stream. An environmental risk assessment of PhACs based on the risk quotient value approach indicated that sulfamethoxazole, ofloxacin and dehydrated erythromycin and clofibric acid had a medium risk, and the mixture of the studied PhACs showed high risk for the aquatic ecosystems. Fourteen out of 21 target PhACs were found in the raw water of the two studied DWTPs with azithromycin having the highest concentration (about 20 ng/L). Following the conventional water treatment processes, the studied PhACs were found to be removed by an average of 54 and 48% in DWTP A and DWTP B, respectively. Among the target PhACs, six and three PhACs were detected in finished water of DWTP B and DWTP A, respectively. This is the first study in southwestern China reporting the presence of PhACs in surface water and drinking water.