Effects and modelling of ultrasonic waste‐activated sludge disintegration

Sonication is a well‐known sludge pretreatment technique with the advantages of simple operation and high efficiency. However, it is an energy‐intensive process. Hence, it is very important to predetermine its sludge disintegration efficiency at varying pretreatment conditions in order to minimize the ultrasonic energy consumption. In this study, it was found that the ultrasonic sludge disintegration occurred in two stages: rapid and subsequent slow disintegration stages. For this reason, it was aimed to develop a simple and accurate mathematical model to describe the two‐stage sludge disintegration as a function of pretreatment conditions. Sludge concentration and ultrasonic density along with sonication period were involved in this model as independent variables. It was determined that the mathematical model can predict accurately the degree of sludge disintegration. Thus, the proposed model was seen to be very useful for evaluating the disintegration efficiency and/or for process design using the operating parameters under different conditions.     

Complexity‐reduced in the hydroclimatological modelling of aquifer's discharge

Climate variability induces considerable interannual fluctuations in spring discharge, especially in mountain areas, where groundwater is recharged mainly by rain and snow melt. This study presents the discharge climatological model (DISCLIM), which was developed to test a complexity‐reduced approach to perform historical reconstruction in the lack of physical assumptions. The Mount Cervialto aquifer (Southern Italy) is the test site, where a powerful karst spring is monitored since the 1920s and is very sensitive to climatic conditions. DISCLIM incorporates seasonal precipitation and climate indicators only. Despite its simplicity, DISCLIM has been able to well estimate the yearly fluctuations of discharge hydrological, explaining about 90% of the interannual variability at the calibration stage, and more than 80% at validation stage. This means that DISCLIM can be easily used for estimating the discharge in historical times, when no all the hydrological balance data are available for the purpose.     

The impact of horizontal water velocity on the energy consumption of a full‐scale wastewater treatment plant

This study quantifies the impact of the oxidation ditch horizontal water velocity on the energy consumption of a full‐scale wastewater treatment plant (WWTP). A WWTP was modelled by means of the ASM2d platform, showing very good correlation with reference operational data. As to reduce the energy requirements of the plant while guarantying the same nutrient removal performance, two approaches were used. A classical aeration control optimization, yielding a 4% energy reduction, was compared with an increase of the oxidation ditch propelling power. The horizontal water velocity in the oxidation ditches was estimated at only 0.15 m/s. By increasing the horizontal water velocity to 0.3 m/s, an 8% energy reduction could be achieved. Moreover, a synergistic effect for classic control optimization and oxidation ditch water velocity optimization resulted in a 14% energy reduction. Energy spent in propelling power counteracts the aeration energy gain derived by an increase in the oxidation ditch horizontal velocity. An optimum in the water velocity was quantified at 0.3 m/s, comparably with its design value.     

Diagnostic test cases for verifying surface heat transfer algorithms and boundary conditions in building energy simulation programs

Verification and validation are crucial in developing and implementing models. Although there are standards to test energy simulation software, this article describes an additional set of eight test cases that are a combination of analytical cases and numerical cases for solid conduction heat transfer. These tests focus on diagnosing and verifying conductive heat transfer algorithms and boundary conditions in building envelopes or fabrics. As an example, EnergyPlus versions 5, 6 and 7 are tested using these eight test cases. The test cases were useful for detecting several bugs in the code. The authors recommend these test cases as useful complements to existing verification test cases for building envelopes.     

Kernel regression for real-time building energy analysis

This study proposes a new technique for real-time building energy modelling and event detection using kernel regression. We show that this technique can exceed the performance of conventional neural network algorithms, and do so by a large margin when the available training dataset is small. Furthermore, unlike the synapse weights in a neural network, the parameters of our kernel regression models are amenable to human interpretation and can give useful information about the building being studied. We extensively test our proposed algorithms using a new dataset consisting of 1.5 years of power and environmental measurements for four buildings, in addition to benchmarking against the ASHRAE Predictor Shootout dataset. On the new dataset, our kernel regression algorithm gave the best prediction performance in three of four cases and significantly outperformed neural networks (the nearest competitor) with training sets of 1/2 a year or less.     

Dynamic thermal building analysis with CFD – modelling radiation

In an attempt to reduce the high computational effort required for dynamic thermal simulation of buildings using computational fluid dynamics (CFD) the authors have recently developed an adaptive freeze-flow method (i.e. freezing of flow equations over variable time periods). This article documents the work that has been carried out to predict the surface heat transfer in dynamic thermal building processes using CFD with particular focus on radiation. The Monte Carlo (MC) and discrete transfer (DT) radiation models were investigated and results compared with analytical solutions. The DT model has shown good performance whereas an unrealistic radiation distribution on the surfaces was observed when using the MC model. A further investigation of the DT model for the cooling of a solid wall has shown that the adaptive freeze-flow method is an efficient and accurate means of conducting dynamic thermal CFD simulations which involve radiation. Finally, application of the technique to a more realistic space comprising an uneven distribution of solar gain showed very good results when compared with a zonal dynamic thermal simulation program.     

Troubleshooting in geoelectrical prospecting using real‐coded genetic algorithm with chromosomal extrapolation

This work presents a genetic operator developed from mathematical methods of curve extrapolation applied in solving problems in geoelectrical prospection. This operator will assist the production of fitter individuals in the population of a genetic algorithm in which inherent patterns of the best individuals of each generation are recognized. The proposed operator in conjunction with a real‐coded genetic algorithm is compared to five alternative optimization techniques known and used in the application to problems in geoelectrical prospection. Copyright © 2014 John Wiley & Sons, Ltd.     

Adsorption for removal of Cd(II) from effluents

The adsorption of Cd(II) onto wollastonite has been reported. Adsorption increased from 55.7 to 93.6% by decreasing the concentration of Cd(II) from 2.0 x 10^‐4M to 0.5 x 10^‐4M. The rearranged Lagergren equation has been used for dynamic modelling of the process. However, the value of rate constant at 30°C was found to be 3.17 x 10^‐2min^‐1. Equilibrium modelling was carried out using the Freundlich isotherm equation and constants have been calculated. Thermodynamic studies were carried out and values of standard free energy (?G°), enthalpy (?H°) and entropy (AS°) were calculated at various temperatures. Low temperatures favour the uptake of Cd(II) in the process.     

Analysing green supply chain management practices in Brazil’s electrical/electronics industry using interpretive structural modelling

Industries need to adopt the environmental management concepts in the traditional supply chain management. The green supply chain management (GSCM) is an established concept to ensure environment-friendly activities in industry. This paper identifies the relationship of driving and dependence that exists between GSCM practices with regard to their adoption within Brazilian electrical/electronic industry with the help of interpretive structural modelling (ISM). From the results, we infer that cooperation with customers for eco-design practice is driving other practices, and this practice acts as a vital role among other practices. Commitment to GSCM from senior managers and cooperation with customers for cleaner production occupy the highest level.     

Measurement and exploration of individual beliefs about the consequences of building information modelling use

Information and communication technology (ICT) is becoming increasingly important in construction although the rate of adoption is considered slow and the industry faces specific implementation challenges. Mainstream information systems research has shown that individuals’ beliefs and expectations of the consequences of ICT use predict subsequent usage. We describe the development of scales to measure beliefs about the consequences of building information modelling (BIM) and their use in a survey of employees of a large construction contracting organization in the United Kingdom. Scales for performance expectancy, effort expectancy, social influence, facilitating conditions, compatibility, and attitude toward using technology were adapted from existing measures. In an analysis of responses from 762 construction employees the scales showed acceptable measurement properties. Expectations about the consequences of BIM use were broadly favourable although there is a need for more data for comparisons. The structure of the responses showed that expectations that BIM would enhance job performance were strongly related to expectations that BIM use was compatible with preferred and existing ways of working. Results also suggest that social influence is complex and may be multidimensional.