Changing the Status Quo in Environmental Engineering Education in Response to Emerging Markets

The job market and skill needs have been expanding for environmental engineers requiring more interdisciplinary training and global citizenship. The 1970s was a decade of regulatory activities, the 1980s was a decade of major environmental disasters, the 1990s was a decade for global awakening, and the first decade of the 21st century is becoming the decade of concern for increasing global environmental stress. In parallel with the environmental trends, the environmental engineering programs have evolved from strictly water/wastewater focus to interdisciplinary programs with a wide selection of courses such as computer science, meteorology, aquatic biology, and ecology in addition to the classical environmental engineering curriculum. Advancements in science and technology, changing demographics, new delivery structures, changes in educational programs and policies, new regulatory requirements, increased global interactions, and recent large scale events with significant environmental impacts have increased the needs for engineers who are trained in environmental engineering discipline with adequate skills for addressing the emerging challenges. This technical note presents the emerging job markets and the corresponding skill needs for environmental engineers to respond to current and evolving environmental challenges both at regional and global scales.     

Biofouling affinity of membrane surfaces under quiescent conditions

Biofouling affinity of four thin film membranes was evaluated under quiescent conditions by exposing the membrane to the effluent from a membrane bioreactor. Coupons of the thin film membranes were left for 5 days in the bioreactor effluent which was filtered through a 0.2 micron filter. The surface morphologies of the membranes before and after the exposure were evaluated by atomic force microscopy (AFM). All membranes showed significant amounts of deposits after the exposure. The easily disturbed nature of the deposits on the polyamide membrane indicated that these deposits were either entrapped within the surface crevices or loosely attached on the membrane. The extent of surface coverage and uniform appearance of the deposits on the polysulfone membrane were indicative of molecular adhesion by functional groups on the membrane surface. Based on the wetting angle measurements, all clean membranes exhibited hydrophilic characteristics. After exposure to the bioreactor effluent, the membranes exhibited complete wetting characteristics. The use of AFM image analysis technique provided insight for the morphological changes and substrate accumulation patterns on membrane surfaces.     

Development of a component-based machining centre selection model using AHP

Machining centres are widely used in manufacturing companies all over the world. Since investments in machining centres are long-term and expensive, selection of the most appropriate machining centre is an important decision for manufacturing companies. There has been considerable effort spent in developing crisp and fuzzy multi-criteria decision-making (MCDM) models that use technical specifications provided by machining-centre manufacturers such as, axis size, power, spindle speed, tolerance, repeatability, cutting-tool change time, and number of cutting tools along with other economical and commercial factors. However, the technical specifications are directly taken from machining-centre manufacturers’ catalogues without checking their correctness, adequacies, or ability to represent the areas that are used for measurement. In such a case, one cannot be sure whether the outcomes are sound or not without a detailed check of the technical specifications, which can only be performed after actual usage of the machine itself. To overcome all such problems, an analytic hierarchy process (AHP) model that evaluates the machining centre components is developed in this paper. The new component-based AHP model is then compared with two MCDM models that use only technical specification values.     

Optimizations of friction stir welding of aluminum alloy by using genetically optimized neural network

Genetically optimized neural network systems (GONNS) was developed to simulate the intelligent decision-making capability of human beings. After they are trained with experimental data or observations, GONNS use one or more artificial neural networks (ANN) to represent complex systems. The optimization is performed by one or more genetic algorithms (GA). In this study, the GONNS was used to estimate the optimal operating condition of the friction stir welding (FSW) process. Five separate ANNs represented the relationship between two identical input parameters and each one of the considered characteristics of the welding zone. GA searched for the optimized parameters to make one of the parameters maximum or minimum, while the other four are kept within the desired range. The GONNS was found as an excellent optimization tool for FSW.     

Effect of Shell Coating Techniques on Dynamic Response of Photoconductive Core/Shell Nanorod Arrays

Efficient carrier collection in the core/shell nanowire (nanorod) arrays requires a high quality interface between core and shell materials. A highly conformal shell layer around nanorods can lead to fast dynamic response in photoconductive devices by a radial charge flow. Therefore, choice of the deposition technique for the conformal shell layer becomes crucial. In this study, the dynamic response of indium sulfide (In₂S₃) nanorods/silver (Ag) core/shell devices is compared in which Ag shell layers are deposited by different physical vapor deposition (PVD) techniques. In₂S₃ nanorods are fabricated by glancing angle deposition. The core/shell devices with Ag shell sputtered at a relatively high working gas pressure (≈3 × 10⁻² mbar) produce the highest photocurrent compared to other devices in which more directional incident flux (with working gas pressure of ≈3 × 10⁻³ mbar) is utilized for Ag shell layer. The reduced transit times indicate a conformal shell achieved by the high pressure sputtering technique that has a wide angular distribution flux. In addition, a more directional flux yet with a small angle (≈30°) incidence with respect to the substrate surface normal also helps increase the photocurrent. Such simple and scalable PVD techniques are shown to offer alternative fabrication approaches in producing high quality core/shell nanostructures.     

A robust Island Parallel Genetic Algorithm for the Quadratic Assignment Problem

The Quadratic Assignment Problem (QAP) is a difficult and important problem studied in the domain of combinatorial optimisation. It is possible to solve QAP instances with 10--20 facilities using exhaustive parallel algorithms within a few days on a cluster machine. However, large QAP instances with more than 100 facilities are not solvable using exhaustive techniques. We have explored a variety of Genetic Algorithm crossover operators for this problem and verified its performance experimentally using well-known instances from the QAPLIB library. By increasing the number of processors, generations and population sizes we have been able to find solutions that are the same as (or very close to) the best reported solutions for large QAP instances in QAPLIB. In order to parallelise the Genetic Algorithm we generate and evolve separate solution pools on each cluster processor, using an island model. This model exchanges 10% of each processor’s solutions at the initial stages of optimisation. We show experimentally that both execution times and solution qualities are improved for large QAP instances by using our Island Parallel Genetic Algorithm.     

Parallel clustering of high dimensional data by integrating multi-objective genetic algorithm with divide and conquer

This paper applies divide and conquer approach in an iterative way to handle the clustering process. The target is a parallelized effective and efficient approach that produces the intended clustering result. We achieve scalability by first partitioning a large dataset into subsets of manageable sizes based on the specifications of the machine to be used in the clustering process; then cluster the partitions separately in parallel. The centroid of each obtained cluster is treated like the root of a tree with instances in its cluster as leaves. The partitioning and clustering process is iteratively applied on the centroids with the trees growing up until we get the final clustering; the outcome is a forest with one tree per cluster. Finally, a conquer process is performed to get the actual intended clustering, where each instance (leaf node) belongs to the final cluster represented by the root of its tree. We use multi-objective genetic algorithm combined with validity indices to decide on the number of classes. This approach fits well for interactive online clustering. It facilitates for incremental clustering because chunks of instances are clustered as stand alone sets, and then the results are merged with existing clusters. This is attractive and feasible because we consider the clustering of only centroids after the first clustering stage. The reported test results demonstrate the applicability and effectiveness of the proposed approach.     

Steam Assisted Hybrid Cooking Behavior of Semitendinosus Muscle: Heterocyclic Amines Formation,Soluble Protein Degradation,Fat Retention,Surface Color,and Cooking Value

Beef (Semitendinosus muscle) was cooked in natural convection, forced convection and steam assisted hybrid oven and saturated steam oven at different oven temperatures until the geometric center of samples reached different end temperatures. Heterocyclic amine (HCA) compounds formation, soluble protein degradation kinetics, cook value, changes in fat content, surface colour and overall acceptance of cooked beef were determined. Soluble protein degradation of beef was considered as first order reaction kinetics and the reaction rate constants, k, were determined in the range of 0.014-0.052 min^?1. In steam assisted hybrid oven had higher reaction rate constants compared to that of the convection ovens. The effect of cooking temperature on soluble protein degradation for natural convection, forced convection and steam assisted hybrid oven followed the Arrhenius type of equation with activation energies of 12.45, 14.57 and 60.16 kJ/mol, respectively. Lower HCAs contents, shorter cooking times, lower cook values and lower fat retention were obtained by steam assisted hybrid oven cooking. Steam assisted hybrid cooking could be considered as an alternative cooking method to obtain a healthier product without compromising the eating habits of conventional methods due to better appearance (moderate burned surface) than saturated steam oven samples and a product retaining the most of the nutritional values.