An advanced simulation model for membrane bioreactors: development, calibration and validation

Synthesis and Evaluation of Asymmetric Mesoporous PTFE/Clay Composite Membranes for Textile Wastewater Treatment

Asymmetric mesoporous composite PTFE membranes wit 40, 50, and 85 wt.% of a clay (kaolin) were fabricated and characterized using a scanning electron microscope equipped with EDX for morphology and elemental analysis. The surface chemistry of the membranes was checked using Fourier transform infrared spectroscopy. The effect of incorporating the clay on the hydrophilicity, permeability, morphology, and antifouling properties of the fabricated membranes was investigated. It was observed that incorporating kaolin particles improved the mechanical properties but decreased the contact angle of the membranes, thereby resulting in an improvement in the membrane permeability. The performance of the three composite UF membranes was evaluated through the treatment of a real textile effluent sample containing indigo dye. The results confirmed that these membranes are effective in the removal of COD, color, and turbidity. Indeed, at a transmembrane pressure of 2.5 bar, almost total removal of the turbidity, COD removal > 85%, and color removal > 97% were attained. Furthermore, membrane A85 (with 85% clay) showed the best performance, with a water flux of 659.1 L·h^-1·m^-2·bar^-1. This study highlights the potential of incorporating low-cost clay material for the enhancement of the performance of mixed organic/inorganic matrix membranes, which can be applied to textile wastewater treatment.

Data-Driven Intelligent Warning Method for Membrane Fouling

Membrane fouling has become a serious issue in membrane bioreactor (MBR) and may destroy the operation of the wastewater treatment process (WWTP). The goal of this article is to design a data-driven intelligent warning method for warning the future events of membrane fouling in MBR. The main novelties of the proposed method are threefold. First, a soft-computing model, based on the recurrent fuzzy neural network (RFNN), was proposed to identify the real-time values of membrane permeability. Second, a multistep prediction strategy was designed to predict the multiple outputs of membrane permeability accurately by decreasing the error accumulation over the predictive horizon. Third, a warning detection algorithm, using the state comprehensive evaluation (SCE) method, was developed to evaluate the pollution levels of MBR. Finally, the proposed method was inserted into a warning system to complete the predicting and warning missions and further tested in the real plants to evaluate its efficiency and effectiveness. Experimental results have verified the benefits of the proposed method.

A mathematical approach to predict the solids concentration in anaerobic membrane bioreactos (AnMBR): Evaluation of the volatile solids solubilization

Anaerobic Membrane Bioreactors (AnMBR) are gaining attention as a suitable approach for sustainable low-strength wastewater treatment, as they bring together the advantages of both anaerobic treatments and membrane bioreactors. However, increasing the sludge retention time (SRT) necessary to favor hydrolysis increases the suspended solids concentration potentially leading to decreased permeate flux. Therefore, the availability of a mathematical approach to predict the solids concentration within an AnMBR can be very useful. In this work, a mathematical model describing the volatile solids concentration within the reactor as a function of the operating parameters and the influent characteristics is developed. The solubilization of organic particulates was clearly influenced by temperature and the SRT, whereas the hydraulic retention time influence was negligible. Furthermore, the activation energy value of about 20 kJ mol^-1 confirms the idea that diffusion of hydrolytic enzymes from the bulk solution to the particle surface is the rate-limiting step of hydrolysis.

A novel protocol for model calibration in biological wastewater treatment

Activated sludge models (ASMs) have been widely used for process design, operation and optimization in wastewater treatment plants. However, it is still a challenge to achieve an efficient calibration for reliable application by using the conventional approaches. Hereby, we propose a novel calibration protocol, i.e. Numerical Optimal Approaching Procedure (NOAP), for the systematic calibration of ASMs. The NOAP consists of three key steps in an iterative scheme flow: i) global factors sensitivity analysis for factors fixing; ii) pseudo-global parameter correlation analysis for non-identifiable factors detection; and iii) formation of a parameter subset through an estimation by using genetic algorithm. The validity and applicability are confirmed using experimental data obtained from two independent wastewater treatment systems, including a sequencing batch reactor and a continuous stirred-tank reactor. The results indicate that the NOAP can effectively determine the optimal parameter subset and successfully perform model calibration and validation for these two different systems. The proposed NOAP is expected to use for automatic calibration of ASMs and be applied potentially to other ordinary differential equations models.