Energy Reduction in Fluidized Bed Bioreactors for Municipal and Ammonia-rich Wastewater Treatment

The combination of biological nutrient removal (BNR) with fluidization technology has demonstrated advantages over suspended growth systems. Previous studies about fluidized bed bioreactor (FBBR) mainly focused on the BNR performance, rarely paid attention to the operation and energy consumption, while high energy consumption is the main hurdle for the industrial application of FBBR systems. In this work, the BNR performance of a novel inverse fluidized bed bioreactor (IFBBR) treating synthetic wastewater was studied. TCOD removal efficiencies of ˃84% were achieved, concomitantly with complete nitrification. Compared with other FBBR systems, the energy consumption for this IFBBR system was an average 59% less. Bacterial community structures of attached and detached biomass revealed that the dominant phyla were Proteobacteria, Bacteroidetes, and Epsilonbacteraeota, etc. The relative abundance of AOB and NOB in aerobic attached biomass were 0.451% and 0.110%, respectively. The IFBBR system was further studied of BNR performance with synthetic high particulate COD wastewater. 87% COD, 73% TN, and 48% TP removal was achieved at OLR of 2.8 kg COD/(m3 d) and nitrogen loading rate (NLR) of 0.26 kg N/(m3 d). Organic shock test was conducted to examine the system sustainability with short term response to the variance of influent COD. A calibrated IFBBR model built in Biowin was efficient to simulate COD and nitrogen concentrations. Although the energy consumption of IFBBR system was reduced, the maximum OLR of 2.8 kg/(m3 d) achieved in the IFBBR system was approximately half of the maximum OLR of 5.3 kg/(m3 d) in the CFBBR system due to high shear force in the aerobic zone and small specific surface area for biomass attachment. The selection of carriers is a crucial issue for FBBRs. Minimum fluidization velocity (Ulmf) affects system design and operation. Four carrier particles (L-HDPE, S-HDPE, pottery, and zeolite) were chosen to study the Ulmf under gas velocities of 0-12.4 mm/s. Partial nitrification (PN) was an alternative way to eliminate ammonia. An FBBR with S-HDPE as carriers was operated to study PN performance at NLRs of 1.2-4.8 kg N/(m3 d). Stable PN was successfully achieved with low effluent NO3-N concentration of \u3c15 mg/L. At NLR of 3.6 kg N/(m3 d), the system effluent NO2-N/NH4-N ratio was 1.27

MHD Simulations on Magnetic Compression of Field Reversed Configurations

The magnetic compression has long been proposed a promising method for the plasma heating in a field reversed configuration (FRC), however, it remains a challenge to fully understand the physical mechanisms underlying the compression process, due to its highly dynamic nature beyond the one-dimensional (1D) adiabatic theory model [R. L. Spencer et al., Phys. Fluids 26, 1564 (1983)]. In this work, magnetohydrodynamics (MHD) simulations on the magnetic compression of FRCs using the NIMROD code [C. R. Sovinec et al., J. Comput. Phys. 195, 355 (2004)] and their comparisons with the 1D theory have been performed. The effects of the assumptions of the theory on the compression process have been explored, and the detailed profiles of the FRC during compression have been investigated. The pressure evolution agrees with the theoretical prediction under various initial conditions. The axial contraction of the FRC can be affected by the initial density profile and the ramping rate of the compression magnetic field, but the theoretical predictions on the FRC's length in general and the relation $r_s=\sqrt{2}r_o$ in particular hold approximately well during the whole compression process, where $r_s$ is the major radius of FRC separatrix and $r_o$ is that of the magnetic axis. The evolutions of the density and temperature can be affected significantly by the initial equilibrium profile and the ramping rate of the compression magnetic field. During the compression, the major radius of the FRC is another parameter that is susceptible to the ramping rate of the compression field. Basically, for the same magnetic compression ratio, the peak density is higher and the FRC's radius $r_s$ is smaller than the theoretical predictions.Comment: 23 pages, 10 figure

Preparation and characterization of poly(vinylidene fluoride) composite membranes blended with nano-crystalline cellulose

AbstractPoly(vinylidene fluoride) (PVDF) composite membranes blended with nano-crystalline cellulose (NCC) for ultrafiltration were prepared by a Loeb–Sourirajan (L–S) phase inversion process. The effects of NCC concentration on the membrane performances were investigated. Surface chemical compositions, surface and cross-section morphologies, degree of crystallinity and the thermal stability of the membranes were characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffraction (XRD) and thermal gravimetric analysis (TGA) respectively. The mechanical properties of the membranes were also investigated. All the experimental results indicated that the properties of the composite membranes were improved due to the addition of NCC. The pure water flux of composite membranes can reach 230.8L/(m2h) and increase up to 47.5% compared with pure PVDF membranes. At the same time, the rejection ratio of a bovine serum albumin solution (1g/L) was up to 92.5%. The porosity and the mean pore size of the composite membranes were 65% and 49nm, respectively. Due to the addition of NCC, the degree of crystallinity was increased to 52.1% resulting in the enhanced mechanical properties. A typical asymmetric structure, which was composed of sponge-like dense layer and finger-like microporous support layer, was observed in SEM images of composite membranes

De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network

Counterfactual inference for continuous rather than binary treatment variables is more common in real-world causal inference tasks. While there are already some sample reweighting methods based on Marginal Structural Model for eliminating the confounding bias, they generally focus on removing the treatment's linear dependence on confounders and rely on the accuracy of the assumed parametric models, which are usually unverifiable. In this paper, we propose a de-confounding representation learning (DRL) framework for counterfactual outcome estimation of continuous treatment by generating the representations of covariates disentangled with the treatment variables. The DRL is a non-parametric model that eliminates both linear and nonlinear dependence between treatment and covariates. Specifically, we train the correlations between the de-confounded representations and the treatment variables against the correlations between the covariate representations and the treatment variables to eliminate confounding bias. Further, a counterfactual inference network is embedded into the framework to make the learned representations serve both de-confounding and trusted inference. Extensive experiments on synthetic datasets show that the DRL model performs superiorly in learning de-confounding representations and outperforms state-of-the-art counterfactual inference models for continuous treatment variables. In addition, we apply the DRL model to a real-world medical dataset MIMIC and demonstrate a detailed causal relationship between red cell width distribution and mortality.Comment: 15 pages,4 figure

Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model

In autonomous navigation settings, several quantities can be subject to variations. Terrain properties such as friction coefficients may vary over time depending on the location of the robot. Also, the dynamics of the robot may change due to, e.g., different payloads, changing the system's mass, or wear and tear, changing actuator gains or joint friction. An autonomous agent should thus be able to adapt to such variations. In this paper, we develop a novel probabilistic, terrain- and robot-aware forward dynamics model, termed TRADYN, which is able to adapt to the above-mentioned variations. It builds on recent advances in meta-learning forward dynamics models based on Neural Processes. We evaluate our method in a simulated 2D navigation setting with a unicycle-like robot and different terrain layouts with spatially varying friction coefficients. In our experiments, the proposed model exhibits lower prediction error for the task of long-horizon trajectory prediction, compared to non-adaptive ablation models. We also evaluate our model on the downstream task of navigation planning, which demonstrates improved performance in planning control-efficient paths by taking robot and terrain properties into account.Comment: \copyright 2023 IEEE. Accepted for publication in European Conference on Mobile Robots (ECMR), 2023. Updated copyright statemen