Treatability of Alternative Fuel Oxygenates Using Advanced Oxidation, Air Stripping, and Carbon Adsorption

Methyl tert-butyl ether (MTBE) is a gasoline oxygenate that has become a significant threat to groundwater supplies across the United States. Due to its physiochemical properties it has proven difficult and costly to remove from contaminated sites. This study was conducted to determine whether the alternative oxygenates (AO)—diisopropyl ether (DIPE), ethyltert-butyl ether (ETBE), tert-amyl methyl ether (TAME), tert-butyl alcohol (TBA), and ethanol (EtOH)—present a more efficient and less costly option from a remediation standpoint. Air stripping, carbon adsorption, and ultraviolet/H2O2 and O3/H2O2 advanced oxidation processes were examined at pilot scale to develop design parameters from which technical and economic comparisons were made for each alternative oxygenate versus MTBE. The experimental results showed that the ether AOs—DIPE, TAME, and ETBE—were each more efficiently and more economically treated than MTBE. The alternative alcohol oxygenates—TBA and EtOH—were less efficiently and less economically treated by the processes studied. The paper details the effects of primary process parameters and properties of individual oxygenates on process efficiency.     

Recent Developments in Predicting Impact and Shock Sensitivities of Energetic Materials （英）

Empirical, quantum mechanical and artificial neural network methods are three usual methods in recent years that were used to predict sensitivity of different classes of high explosives. Some recent developments in predicting sensitivity by various methods are reviewed and discussed for various classes of energetic materials.     

Enzyme-assisted extraction of chicken skin protein hydrolysates and fat: Degree of hydrolysis affects the physicochemical and functional properties

Enzyme-assisted extraction (EAE) of oils/fats involves the disruption of the cell wall of source material using enzymes to facilitate the release of oil. When proteases are used as the enzyme, EAE ends in the extracted oil as well as the protein hydrolysates. Herein, the EAE (using a commercial protease, Alcalase) was exploited to obtain fat and protein hydrolysates from chicken skin. Degree of hydrolysis (DH, the percentage ratio of cleaved peptide bonds), which showed a logarithmic correlation with the reaction time, was found to affect the properties of the products. As the DH increased, the peptide chain length of protein hydrolysates decreased which was confirmed by SDS-PAGE analysis. With the increase of DH, the emulsifying activity index, foaming capacity, and oil holding capacity of the hydrolysates decreased but the solubility and emulsion stability index increased (p < 0.05). The DPPH free radical scavenging activity of the hydrolysates increased with the DH up to DH = 39.62% but decreased thereafter (p < 0.05). EAE resulted in a rise in fat yield and the fat contained a higher amount of unsaponifiables and lower free fatty acids (FFA) content, as compared to the control treatment (No enzyme, 80°C, 2 h, p < 0.05). DH affected the fat yield and the unsaponifiables content of the fat, positively (p < 0.05). However, it did not affect the fat FFA content and iodine value (p > 0.05). Results obtained here showed DH can be used as an effective measure for controlling the physicochemical and functional properties of chicken skin protein hydrolysates and fat in the EAE process.     

AZ31-O镁合金和6061-T6铝合金搅拌摩擦焊接头的显微组织和力学性能(英文)

对3 mm厚的AZ31-O镁合金板和6061-T6铝合金板进行异种材料搅拌摩擦焊。实验中搅拌针的旋转速度为600~1400 r/min,前进速度为20~60 mm/min。结果表明,在旋转速度1000 r/min、前进速度40 mm/min的工艺条件下,可以获得无缺陷的焊接接头。组织观察发现搅拌区的晶粒尺寸要比基材区的明显小很多。在搅拌区形成了复杂的金属流动。显微硬度测试表明搅拌区的硬度分别是不均匀的。拉伸实验结果表明,接头的拉伸强度约为基材AZ31镁合金的76%,或6061铝合金的60%。接头拉伸断口SEM形貌观察表明为脆性断裂。     

Performance Assessment of a Coupled Particle Swarm Optimization and Network Flow Programming Model for Optimum Water Allocation

Water resources allocation problems are mainly categorized in two classes of simulation and optimization. In most cases, optimization problems due to the number of variables, constraints and nonlinear feasible search space are known as a challenging subject in the literature. In this research, by coupling particle swarm optimization (PSO) algorithm and a network flow programming (NFP) based river basin simulation model, a PSO-NFP hybrid structure is constructed for optimum water allocation planning. In the PSO-NFP model, the NFP core roles as the fast inner simulation engine for finding optimum values for a large number of water discharges in the network links (rivers and canals) and nodes (reservoirs and demands) while the heuristic PSO algorithm forms the outer optimization cover to search for the optimum values of reservoirs capacities and their storage priorities. In order to assess the performance of the PSO-NFP model, three hypothetical test problems are defined, and their equivalent nonlinear mathematical programs are developed in LINGO and the results are compared. Finally, the PSO-NFP model is applied in solving a real river basin water allocation problem. Results indicate that the applied method of coupling PSO and NFP has an efficient ability for handling river basin-scale water resources optimization problems.     

Sustainable Basin-Scale Water Allocation with Hydrologic State-Dependent Multi-Reservoir Operation Rules

This study extends the PSO-MODSIM model, integrating particle swarm optimization (PSO) algorithm and MODISM river basin decision support system (DSS) to determine optimal basin-scale water allocation, in two aspects. The first is deriving hydrologic state-dependent (conditional) operating rules to better account for drought and high-flow periods, and the second is direct, explicit consideration of sustainability criteria in the model’s formulation to have a better efficiency in basin-scale water allocation. Under conditional operating rules, the operational parameters of reservoir target storage levels and their priority rankings were conditioned on the hydrologic state of the system in a priority-based water allocation scheme. The role of conditional operating rules and policies were evaluated by comparing water shortages associated with objective function values under unconditional and conditional operating rules. Optimal basin-scale water allocation was then evaluated by incorporating reliability, vulnerability, reversibility and equity sustainability indices into the PSO objective function. The extended model was applied for water allocation in the Atrak River Basin, Iran. Results indicated improved distribution of water shortages by about 7.5% using conditional operating rules distinguishing dry, normal and wet hydrologic states. Alternative solutions with nearly identical objective function values were found with sustainability indices included in the model.     

Real-Time Operation of Pumping Systems for Urban Flood Mitigation: Single-Period vs. Multi-Period Optimization

To reduce flood risk in urban regions, it is important to optimize the performance of operational elements such as gates and pumps. This paper compares the performances of two approaches of multi-period and single-period simulation-optimization that are used to derive real-time control policies for operating urban drainage systems. The EPA storm water management model (SWMM), converting real-time rainfall data to surface runoff at network control points, i.e. pump stations, is linked to the particle swarm optimization (PSO) algorithm, evaluating the system operation performance measure (objective function) for different sets of control policies. A prototype network in a portion of the Seoul urban drainage system is used to investigate the efficiency of the proposed approaches. Results justify the high efficiency of multi-period optimization, leading to 32 and 29% average reductions in peak water level violations from a pre-defined permissible threshold at target points and the number of pump switches, respectively, in comparison with the online single-period optimization. The myopic policies derived by single-period optimization are not reliable, and in some cases, they even perform worse than ad-hoc policies applied by system operators based on their past experiences.     

Novel Approach to Integration of Numerical Modeling and Field Observations for Deep Excavations

Precedent and observation of performance are an essential part of the design and construction process in geotechnical engineering. For deep urban excavations designers rely on empirical data to estimate potential deformations and impact on surrounding structures. Numerical simulations are also employed to estimate induced ground deformations. Significant resources are dedicated to monitor construction activities and control induced ground deformations. While engineers are able to learn from observations, numerical simulations have been unable to fully benefit from information gained at a given site or prior excavation case histories in the same area. A novel analysis method, self-learning in engineering simulations (SelfSim), is introduced to integrate precedent into numerical simulations. SelfSim is an inverse analysis technique that combines finite element method, biologically inspired material models, and field measurements. SelfSim extracts relevant constitutive soil information from field measurements of excavation response such as lateral wall deformations and surface settlement. The resulting soil model, used in a numerical analysis, provides correct ground deformations and can be used in estimating deformations of similar excavations. The soil model can continuously evolve using additional field information. SelfSim is demonstrated using two excavation case histories in Boston and Chicago.