Integrated pretreatment with capacitive deionization for reverse osmosis reject recovery from water reclamation plant

Reverse osmosis (RO) reject recovery from the water reclamation process was demonstrated feasible using an integrated pretreatment scheme followed by the Capacitive Deionization (CDI) process. The RO reject had an average total dissolved solids (TDS) of 1276 ± 166 mg/L. Water recovery of 85% with water quality comparable with the RO feed was achieved. Pretreatments using biological activated carbon (BAC) and BAC–ultrafiltration (UF) attained total organic carbon (TOC) removal efficiencies of 23.5 ± 6.0% and 39.9 ± 9.0%, respectively. Organics removal of RO reject was attributed to simultaneous adsorption and biodegradation in the BAC pretreatment, while further biodegradation in the submerged UF membrane tank provided additional organics removal. Membrane and CDI fouling was reduced by pH adjustment of the pretreated RO reject to approximately 6.5, which prolonged the CDI operation time by at least two times. The CDI process was able to achieve more than 88 and 87% TDS and ion removals, respectively, while PO₄³⁻ and TOC removals were at 52–81% and 50–63%, respectively.     

Syngas production from CH4 dry reforming over Co–Ni/Al2O3 catalyst: Coupled reaction-deactivation kinetic analysis and the effect of O2 co-feeding on H2:CO ratio

The dry and oxidative dry reforming of CH₄ over alumina-supported Co–Ni catalysts were investigated over 72-h longevity experiments. The deactivation behaviour at low CO₂:CH₄ ratio (≤2) suggests that carbon deposition proceeds via a rapid dehydropolymerisation step resulting in the blockage of active sites and loss in CO₂ consumption. In particular, at high temperatures of 923 K and 973 K, a ‘breakthrough’ point was observed in which deactivation that was previously slow suddenly accelerated, indicating rapid polymerisation of deposited carbon. Only with feed CO₂:CH₄ > 2 or with O₂ co-feeding was coke-induced deactivation eliminated. In particular, O₂ co-feeding gave improved carbon removal, product H₂:CO ratios more suitable for downstream GTL processing and stable catalytic performance. Conversion-time data were adequately fitted to the generalised Levenspiel reaction-deactivation model. Activation energy estimate (66–129 kJ mol⁻¹) was dependent on the CO₂:CH₄ ratio but representative of other hydrocarbon reforming reactions on Ni-based catalysts.     

Energy consumption and CO2 emissions in Turkey: Empirical analysis and future projection based on an economic growth

In this study, Turkey's energy sector was overviewed during the period of 1970–2002. The total energy consumption (TEC) was modeled by using the economic growth (proxied by gross national product—GNP) and population increase, which are the two important factors to determine the energy consumption for developing countries. In addition, the relationship between the TEC and total CO₂ (TCO₂) emission was studied. For this purpose, regression analysis was performed and the strong relationship between TEC and TCO₂ (R²=0.998$R^2=0.998$) was modeled. Also, results showed that a regression model can be used to predict the TEC from the country population and the GNP with high confidence (R²=0.996$R^2=0.996$).     

Multifilter rotating shadowband radiometer calibration for spectral aerosol optical depth retrievals over S?o Paulo City, Brazil

Multifilter rotating shadowband radiometer (MFRSR) calibration values for aerosol optical depth (AOD) retrievals were determined by means of the general method formulated by Forgan [Appl. Opt.33, 4841 (1994)] at a polluted urban site. The obtained precision is comparable with the classical method, the Langley plot, applied on clean mountaintops distant of pollution sources. The AOD retrieved over S?o Paulo City with both calibration procedures is compared with the Aerosol Robotic Network data. The observed results are similar, and, except for the shortest wavelength (415 nm), the MFRSR's AOD is systematically overestimated by approximately 0.03.     

A computer-based system for environmental impact assessment (EIA) applications to energy power stations in Turkey: ÇEDINFO

Environmental impact assessment (EIA) is a tool for decision makers to take into account the possible effects of a proposed project on the environment and is also a process for collecting the data related to a project design and project area. Different techniques are used for the EIA process. In recent years, including the design and development of databases, classification systems, computer models and expert systems have been used extensively in impact assessment studies. Knowledge-based systems referred to as expert systems and different computer-based systems are an emerging technology in information processing and are becoming increasingly useful tools in different applications areas including EIA studies. Their use for EIA has been quite limited in developing countries, because of the constraints on resources, particularly in expertise and data.

In this study, a knowledge-based software—ÇEDINFO—developed by authors was introduced. ÇEDINFO to be used for EIA practices on energy-generating stations was designed based on the legal EIA process in Turkey. According to the EIA Regulation enacted in Turkey in 1993, energy-generating stations (thermal power station, hydraulic station, nuclear station) in different categories require mandatory EIA reports duly approved by The Ministry of Environment and Forestry before their construction. ÇEDINFO primarily aims to provide educational support for EIA practices and decision-makers on energy-generating stations.     

Shapeme histogram projection and matching for partial object recognition

Histograms of shape signature or prototypical shapes, called shapemes, have been used effectively in previous work for 2D/3D shape matching and recognition. We extend the idea of shapeme histogram to recognize partially observed query objects from a database of complete model objects. We propose representing each model object as a collection of shapeme histograms and match the query histogram to this representation in two steps: 1) compute a constrained projection of the query histogram onto the subspace spanned by all the shapeme histograms of the model and 2) compute a match measure between the query histogram and the projection. The first step is formulated as a constrained optimization problem that is solved by a sampling algorithm. The second step is formulated under a Bayesian framework, where an implicit feature selection process is conducted to improve the discrimination capability of shapeme histograms. Results of matching partially viewed range objects with a 243 model database demonstrate better performance than the original shapeme histogram matching algorithm and other approaches.     

Unsupervised learning of discriminative edge measures for vehicle matching between nonoverlapping cameras

This paper proposes a novel unsupervised algorithm learning discriminative features in the context of matching road vehicles between two non-overlapping cameras. The matching problem is formulated as a same-different classification problem, which aims to compute the probability of vehicle images from two distinct cameras being from the same vehicle or different vehicle(s). We employ a novel measurement vector that consists of three independent edge-based measures and their associated robust measures computed from a pair of aligned vehicle edge maps. The weight of each measure is determined by an unsupervised learning algorithm that optimally separates the same-different classes in the combined measurement space. This is achieved with a weak classification algorithm that automatically collects representative samples from same-different classes, followed by a more discriminative classifier based on Fisher' s Linear Discriminants and Gibbs Sampling. The robustness of the match measures and the use of unsupervised discriminant analysis in the classification ensures that the proposed method performs consistently in the presence of missing/false features, temporally and spatially changing illumination conditions, and systematic misalignment caused by different camera configurations. Extensive experiments based on real data of over 200 vehicles at different times of day demonstrate promising results.     

Performance of an ultra-compact biofilm reactor treating domestic and synthetic wastewaters

The performance of an ultra-compact biofilm reactor (UCBR) treating domestic wastewater (DWW) collected from a local water reclamation plant; and gradually shifting to a mono-type carbon source synthetic wastewater (SWW) combined with DDW (CWW) and finally SWW; was investigated in this study. The total COD concentrations of influent DWW and CWW/SWW were 413.6 ± 80.8 mg/L and 454.9 ± 51.3 mg/L, respectively. The UCBR was able to achieve average total COD removal efficiencies of 70 ± 10% and 80 ± 4% for DWW and SWW respectively. The total COD concentrations of the effluent of DWW and CWW/SWW were 122.5 ± 44.4 mg/L and 89.7 ± 10.3 mg/L, respectively. These observations suggested that heterotrophs in the UCBR system were able to better assimilate and remove carbon of mono-type SWW compared to diverse carbon sources such as DWW; although the influent soluble COD concentrations of the SWW were higher than those of the DWW. However, the effluent NH(4)(+)-N concentrations for both types of wastewater were rather similar, <3.0 mg/L; although the influent NH(4)(+)-N concentrations of the DWW were 1.5 times those of the SWW.     

Adaptive neuro-fuzzy inference system modeling of cryogenically treated AISI M2 HSS turning tool for estimation of flank wear

This study deals with modeling the flank wear of cryogenically treated AISI M2 high speed steel (HSS) tool by means of adaptive neuro-fuzzy inference system (ANFIS) approach. Cryogenic treatment has recently been found to be an innovative technique to improve wear resistance of AISI M2 HSS tools but precise modelling approach which also incorporates the cryogenic soaking temperature to simulate the tool flank wear is still not reported in any open literature. In order to obtain data for developing the ANFIS model, turning of hot rolled annealed steel stock (C-45) by cryogenically treated tools treated at various cryogenic soaking temperatures was performed in steady state conditions while varying the cutting speed and cutting time. The model combined modeling function of fuzzy inference with the learning ability of artificial neural network; and a set of rules has been generated directly from experimental data. It was determined that the predictions usually agreed well with the experimental data with correlation coefficients of 0.994 and mean errors of 2.47%. The proposed model can also be used for estimating tool flank wear on-line but the accuracy of the model depends upon the proper training and selection of data points.