Evaluating the Feasibility of Forward Osmosis in Diluting RO Concentrate Using Pretreatment Backwash Water

Forward osmosis (FO) is an excellent membrane process to dilute seawater (SW) reverse osmosis (RO) concentrate for either to increase the water recovery or for safe disposal. However, the low fluxes through FO membranes as well the biofouling/scaling of FO membranes are bottlenecks of this process requiring larger membrane area and membranes with anti-fouling properties. This study evaluates the performance of hollow fibre and flat sheet membranes with respect to flux and biofouling. Ferric hydroxide sludge was used as impaired water mimicking the backwash water of a filter that is generally employed as pretreatment in a SWRO plant and RO concentrate was used as draw solution for the studies. Synthetic salts are also used as draw solutions to compare the flux produced. The study found that cellulose triacetate (CTA) flat sheet FO membrane produced higher flux (3-6 L m-2 h-1) compared to that produced by polyamide (PA) hollow fibre FO membrane (less than 2.5 L m-2 h-1) under the same experimental conditions. Therefore, long-term studies conducted on the flat sheet FO membranes showed that fouling due to ferric hydroxide sludge did not allow the water flux to increase more than 3.15 L m-2 h-1

Optimal designs for an additive quadratic mixture model involving the amount of mixture

This paper is concerned with D- and A-optimal designs for a quadratic additive model for experiments with mixtures, in which the response depends not only on the relative proportions but also on the actual amounts of the mixture components. It is found that the origin and vertices of the simplex are support points of these optimal designs, and when the number of mixture components increases, other support points shift gradually from barycentres of depth 1 to barycentres of higher depths. It is shown that the D-optimal designs have high efficiency in terms of A-optimality, and vice versa.published_or_final_versio

Effects of Landscape Design on Urban Microclimate and Thermal Comfort in Tropical Climate

A climate-responsive landscape design can create a more livable urban microclimate with adequate human comfortability. This paper aims to quantitatively investigate the effects of landscape design elements of pavement materials, greenery, and water bodies on urban microclimate and thermal comfort in a high-rise residential area in the tropic climate of Singapore. A comprehensive field measurement is undertaken to obtain real data on microclimate parameters for calibration of the microclimate-modeling software ENVI-met 4.0. With the calibrated ENVI-met, seven urban landscape scenarios are simulated and their effects on thermal comfort as measured by physiologically equivalent temperature (PET) are evaluated. It is found that the maximum improvement of PET reduction with suggested landscape designs is about 12°C, and high-albedo pavement materials and water bodies are not effective in reducing heat stress in hot and humid climate conditions. The combination of shade trees over grass is the most effective landscape strategy for cooling the microclimate. The findings from the paper can equip urban designers with knowledge and techniques to mitigate urban heat stress

Ca isotope constraints on chemical weathering processes: Evidence from headwater in the Changjiang River, China

This study aims to clarify the relationship between chemical weathering of rocks and the carbon budget of rivers and better understand the weathering mechanisms of plateau watersheds. We chose to study the Jinsha River, which originates from the Tibetan Plateau and also is in the upper reaches of the Changjiang River. Analysis of hydrochemistry, radiogenic strontium isotope and stable calcium isotopes were conducted of the Jinsha River water samples, which were collected along its mainstream and main tributaries in the summer. The results show that the water chemistry of the mainstream waters is dominated by evaporite weathering, which have low 87Sr/86Sr values (0.7098–0.7108) and wide range of Sr contents (2.70–9.35 μmol/L). In contrast, tributaries of the Jinsha River have higher 87Sr/86Sr (0.7090–0.7157) and lower Sr contents (∼1 μmol/L). Moreover, the Ca isotopic compositions in the mainstream (0.87–1.11‰) are heavier than the tributaries (0.68–0.88‰) and could not be fully explained by the conventional mixing of different sources. We suggest that secondary carbonate precipitation fractionates Ca isotopes in the Jinsha River, and fractionation factors are between 0.99935 and 0.99963. At least 66% of Ca was removed in the mainstream of the Jinsha River through secondary mineral precipitation, and the average value is ∼35% in the tributaries. The results highlight that evaporite weathering results in more carbonate precipitation influencing Ca transportation and cycling in the riverine system constrained by stable Ca isotopic compositions and water chemistry

Design optimization considering variable thermal mass, insulation, absorptance of solar radiation, and glazing ratio using a prediction model and genetic algorithm

This paper presents the optimization of building envelope design to minimize thermal load and improve thermal comfort for a two-star green building in Wuhan, China. The thermal load of the building before optimization is 36% lower than a typical energy-efficient building of the same size. A total of 19 continuous design variables, including different concrete thicknesses, insulation thicknesses, absorbance of solar radiation for each exterior wall/roof and different window-to-wall ratios for each façade, are considered for optimization. The thermal load and annual discomfort degree hours are selected as the objective functions for optimization. Two prediction models, multi-linear regression (MLR) model and an artificial neural network (ANN) model, are developed to predict the building thermal performance and adopted as fitness functions for a multi-objective genetic algorithm (GA) to find the optimal design solutions. As compared to the original design, the optimal design generated by the MLRGA approach helps to reduce the thermal load and discomfort level by 18.2% and 22.4%, while the reductions are 17.0% and 22.2% respectively, using the ANNGA approach. Finally, four objective functions using cooling load, heating load, summer discomfort degree hours, and winter discomfort degree hours for optimization are conducted, but the results are no better than the two-objective-function optimization approach

A Review of Recent Advances in Research on PM2.5 in China

PM2.5 pollution has become a severe problem in China due to rapid industrialization and high energy consumption. It can cause increases in the incidence of various respiratory diseases and resident mortality rates, as well as increase in the energy consumption in heating, ventilation, and air conditioning (HVAC) systems due to the need for air purification. This paper reviews and studies the sources of indoor and outdoor PM2.5, the impact of PM2.5 pollution on atmospheric visibility, occupational health, and occupants’ behaviors. This paper also presents current pollution status in China, the relationship between indoor and outdoor PM2.5, and control of indoor PM2.5, and finally presents analysis and suggestions for future research

Development of Building Thermal Load and Discomfort Degree Hour Prediction Models Using Data Mining Approaches

Thermal load and indoor comfort level are two important building performance indicators, rapid predictions of which can help significantly reduce the computation time during design optimization. In this paper, a three-step approach is used to develop and evaluate prediction models. Firstly, the Latin Hypercube Sampling Method (LHSM) is used to generate a representative 19-dimensional design database and DesignBuilder is then used to obtain the thermal load and discomfort degree hours through simulation. Secondly, samples from the database are used to develop and validate seven prediction models, using data mining approaches including multilinear regression (MLR), chi-square automatic interaction detector (CHAID), exhaustive CHAID (ECHAID), back-propagation neural network (BPNN), radial basis function network (RBFN), classification and regression trees (CART), and support vector machines (SVM). It is found that the MLR and BPNN models outperform the others in the prediction of thermal load with average absolute error of less than 1.19%, and the BPNN model is the best at predicting discomfort degree hour with 0.62% average absolute error. Finally, two hybrid models—MLR (MLR + BPNN) and MLR-BPNN—are developed. The MLR-BPNN models are found to be the best prediction models, with average absolute error of 0.82% in thermal load and 0.59% in discomfort degree hour