Modeling and multi-objective optimization for ANAMMOX process under COD disturbance using hybrid intelligent algorithm.

Anaerobic ammonium oxidation (ANAMMOX) has been regarded as an efficient process to treat nitrogen-containing wastewater. However, the treatment process is not fully understood in terms of reaction mechanisms, process simulation, and control. In this paper, a multi-objective control strategy mixed soft-sensing model (MCSSM) is developed to systematically design the operating variations for multi-objective control by integrating the developed model, a least square support vector machine optimized with principal component analysis (PCA-LSSVM) and non-dominated sorting genetic algorithm-II (NSGA-II). The results revealed that the PCA-LSSVM model is a feasible and efficient tool for predicting the effluent ammonia nitrogen concentration ([Formula: see text]) and the total nitrogen removal concentration (CTN, rem) with determination coefficients (R2) were 0.997 for [Formula: see text] and 0.989 for CTN, rem, and gives us the reasonable solutions in influent by using NSGA-II. To achieve a better removal effect, the influent pH should be kept between 7.50 and 7.52, the COD/TN ratio is suggested to maintain at 0.15 and the NH4+-N/NO2--N ratio is suggested to maintain at 0.61. The developed MCSSM approach and its general modeling framework have a high potential of applicability and guidance to bioprocess in wastewater treatment, and numerical models can be structured for predicting and optimization and experiments can be conducted for data acquisition and model establishment.

[MIL-88A@MIP Activated Persulfate for Targeted Degradation of Dibutyl Phthalate].

MIL-88A@MIP was fabricated for the first time in this experiment with a metal-organic framework of MIL-88A as the precursor based on the molecular imprinting method. It was characterized by X-ray diffraction (XRD), scanning electronic microscopy (SEM), energy dispersive spectrometer (EDS), and N2 adsorption. The catalytic performance of MIL-88A@MIP was tested to activate persulfate (PS) to generate SO4-· for the degradation of dibutyl phthalate (DBP), which was used as a target pollutant. Compared with the precursor MIL-88A, the catalytic activity of MIL-88A@MIP was improved effectively through targeted modification, and the DBP removal rate increased 80.4% after reacting for 480 min. An experiment determining the influencing factors showed that the optimum activation condition of the catalyst was PS:DBP=600:1, MIL-88A@MIP dosage of 0.5 g·L-1,and pH=3.26. Furthermore, MIL-88A@MIP shows a high capability of removing different phthalic acid ester (PAE) contaminants that reflect its targeting selectivity.

[Accuracy of predicting in vitro ruminal methane production in goats using volatile fatty acids stoichiometric models].

This study was conducted to investigate the accuracy of predicting in vitro ruminal methane (CH4) production using volatile fatty acids (VFA) stoichiometric models [CH4 = 0.5Ace-0.25Pro + 0.5But-0.25Val] (model 1), where CH4, Ace, Pro, But and Val are the production amounts of CH4, acetate, propionate, butyrate and valerate, respectively. Ten common feedstuffs, including four concentrates and six roughages with a wide range of chemical composition were incubated in serum bottles, and VFAs and CH4 production at 72 h were determined. The differences between the predicted and measured CH4 production were quantified using the model accuracy analysis. The results showed that the predicted CH4 production amounts were generally greater than the measured values obtained using the model 1, and the bias, slope and random error were 62.6%, 11.7% and 25.7%, respectively, indicating that fixed error exceeded 70%. By assuming 80% of total hydrogen being used for CH4 synthesis, the VFA stoichiometric model could be re-expressed as [CH4 = 0.8 (0.5Ace-0.25Pro + 0.5But-0.25Val)] (model 2). The root mean square prediction error (rMSPE = 0.18) for model 2 was less than for model 1 (rMSPE = 0.60). In addition, the bias, slope and random error of the model 2 were 2.1%, 5.7%, 92.3%, respectively, indicating that fixed error was less than 10%. In model 1, hydrogen formation resulting from VFA production were assumed to be totally consumed by methanogens for CH4 synthesis, without considering other pathways of hydrogen metabolism, which was the main factor resulting in the higher predicted values than the measured values.