A multi-layer feed forward neural network model for accurate prediction of flue gas sulfuric acid dew points in process industries

Acidic combustion gases can cause rapid corrosion when they condense on pollution control or energy recovery equipments. Since the potential of sulfuric acid condensation from flue gases is of considerable economic significance, a multi-layer feed forward artificial neural network has been presented for accurate prediction of the flue gas sulfuric acid dew points to mitigate the corrosion problems in process and power plants. According to the network’s training, validation and testing results, a three layer neural network with four neurons in the hidden layer is selected as the best architecture for accurate prediction of sulfuric acid dew points. The presented model is very accurate and reliable for predicting the acid dew points over wide ranges of sulfur trioxide and water vapor concentrations. Comparison of the suggested neural network model with the most important existing correlations shows that the proposed neuromorphic model outperforms the other alternatives both in accuracy and generality. The predicted flue gas sulfuric acid dew points are in excellent agreement with experimental data suggesting the accuracy of the proposed neural network model for predicting the sulfuric acid condensation in stacks, pollution control devices, economizers and flue gas recovery systems in process industries.     

A practical algorithm for distribution state estimation including renewable energy sources

Renewable energy is energy that is in continuous supply over time. These kinds of energy sources are divided into five principal renewable sources of energy: the sun, the wind, flowing water, biomass and heat from within the earth. According to some studies carried out by the research institutes, about 25% of the new generation will be generated by Renewable Energy Sources (RESs) in the near future. Therefore, it is necessary to study the impact of RESs on the power systems, especially on the distribution networks. This paper presents a practical Distribution State Estimation (DSE) including RESs and some practical consideration. The proposed algorithm is based on the combination of Nelder–Mead simplex search and Particle Swarm Optimization (PSO) algorithms, called PSO-NM. The proposed algorithm can estimate load and RES output values by Weighted Least-Square (WLS) approach. Some practical considerations are var compensators, Voltage Regulators (VRs), Under Load Tap Changer (ULTC) transformer modeling, which usually have nonlinear and discrete characteristics, and unbalanced three-phase power flow equations. The comparison results with other evolutionary optimization algorithms such as original PSO, Honey Bee Mating Optimization (HBMO), Neural Networks (NNs), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) for a test system demonstrate that PSO-NM is extremely effective and efficient for the DSE problems.     

Configurable three-way model merging

Software development is a collaborative activity that requires teams of software engineers to cooperate and work in parallel on versions of models. However, model management techniques such as model differencing, merging, and versioning have turned out to be difficult challenges, due to the complexity of operations and graph-like nature of models. Therefore, a well-developed support for model merging process, as well as conflict management, is highly desired. This paper presents a novel process for model merging, called the Epsilon-based Three-way Merging Process (E3MP) process. Model merging is a significant problem where there are different versions of a system model amongst modeler teams. E3MP includes three components implemented into the Epsilon framework. First, modelers can define domain-specific rules that customize the merging process. Second, E3MP enables an automated method for syntactic and semantic conflict detection amongst different versions of the system model. Third, E3MP puts forward a pattern-based approach for conflict resolution. We applied two generic benchmarks to assess conflict detection and resolution capabilities of our approach and carried out an initial scalability evaluation for the model merge with large models and large change sets. The results of our experiments revealed that the proposed process allows generating consistent and semantically correct merged models.     

Stress test optimization using an integrated production test and field reliability model

Numerous papers and texts have been written in the reliability literature regarding the determination of the optimum test duration for a production stress or a burn‐in test. The techniques presented have largely been based on the identification of the change point at which infant mortality has largely been removed from the units. The time‐on‐test is typically the only factor that influences this decision. Few of these models have attempted to integrate the field performance or the influence of warranty costs into this decision. This paper proposes and validates a methodology that integrates the influence of the production test failures and the field performance including their respective costs into a single unified model. The objective is to identify a production test duration that minimizes the overall cost. A Weibull model is initially developed for the production test that incorporates the failure observations in different time segments of the test based on the ability to detect latent defects in the product. A separate Weibull model is then developed for the product's performance in the field that includes the lifetime of the unit. This paper identifies how both these Weibull models can be combined into a single model including both test and field costs with the objective of minimizing the overall cost. The advantage of the proposed technique is that it does not require one to track individual units from production through to the field in order to develop an integrated test and field cost model. Copyright © 2009 John Wiley & Sons, Ltd.     

A hybrid evolutionary algorithm for distribution feeder reconfiguration

Distribution feeder reconfiguration (DFR) is formulated as a multi-objective optimization problem which minimizes real power losses, deviation of the node voltages and the number of switching operations and also balances the loads on the feeders. In the proposed method, the distance (λ ₂ norm) between the vector-valued objective function and the worst-case vector-valued objective function in the feasible set is maximized. In the algorithm, the status of tie and sectionalizing switches are considered as the control variables. The proposed DFR problem is a non-differentiable optimization problem. Therefore, a new hybrid evolutionary algorithm based on combination of fuzzy adaptive particle swarm optimization (FAPSO) and ant colony optimization (ACO), called HFAPSO, is proposed to solve it. The performance of HFAPSO is evaluated and compared with other methods such as genetic algorithm (GA), ACO, the original PSO, Hybrid PSO and ACO (HPSO) considering different distribution test systems.     

Mechanochemical synthesis and structural characterization of nano-sized amorphous tricalcium phosphate

Nano-sized amorphous tricalcium phosphate powders were synthesized through different mechanochemical reactions. The influence of milling parameters and chemical composition of reagents on the formation of amorphous tricalcium phosphate was investigated. In all the experiments, the mole ratio of calcium to phosphorous oxide was 3:1, i.e. the stoichiometric Ca/P content in the composition of amorphous tricalcium phosphate (Ca/P=1.5). Results revealed that the phase purity, structural features, and morphological characteristics of products were significantly influenced by the chemical composition of raw materials and milling parameters. For all the reactions, amorphous tricalcium phosphate was formed as the main product of mechanical activation after 10 h. After annealing at 1100 °C, crystallization of amorphous phase occurred, and consequently high crystalline β-tricalcium phosphate was generated. According to FT-IR findings, the synthesized powders had high chemical purity. After 10 h of milling, the obtained nanopowders through four distinct reactions exhibited crystallite sizes about 20, 69, 58 and 55 nm. The results from scanning electron micrographs showed that the mean size of agglomerate was in the range of 1–5 μm. Detailed study of morphological features by using transmission electron microscopy confirmed the formation of nano-sized amorphous tricalcium phosphate with spheroidal and ellipse-like morphologies.     

Nitric oxide-releasing porous silicon nanoparticles

In this study, the ability of porous silicon nanoparticles (PSi NPs) to entrap and deliver nitric oxide (NO) as an effective antibacterial agent is tested against different Gram-positive and Gram-negative bacteria. NO was entrapped inside PSi NPs functionalized by means of the thermal hydrocarbonization (THC) process. Subsequent reduction of nitrite in the presence of d-glucose led to the production of large NO payloads without reducing the biocompatibility of the PSi NPs with mammalian cells. The resulting PSi NPs demonstrated sustained release of NO and showed remarkable antibacterial efficiency and anti-biofilm-forming properties. These results will set the stage to develop antimicrobial nanoparticle formulations for applications in chronic wound treatment.     

Characteristics of Ni/YSZ ceramic anode prepared using carbon microspheres as a pore former

Microstructural features and physical properties of the anodes crucially affect the electrochemical performance of anode-supported solid oxide fuel cells (SOFCs). This paper evaluated the microstructural characteristics and properties including porosity, pore size distribution, sintering shrinkage, mechanical strength, and electrical conductivity of the SOFC anode using carbon microspheres (CMSs) as the pore-former in the fabrication of Ni/YSZ ceramic anode. CMSs with different average particle sizes (CMS1: 11.54 μm, CMS2: 4.39 μm, and CMS3: 0.27 μm) were synthesized, and then incorporated into NiO/YSZ at various volumetric blend ratios ranging from 4.4 to 44.6 vol.%. SOFC anode cermets with a desirable range of porosity (30–40%), shrinkage (15.9–17.3%), flexural strength (75.4–157.8 N), and electrical conductivity (253.5–510.7 S/cm) were obtained using approximately 4–10 vol% of CMS1, 4–20 vol.% of CMS2, and 10–34 vol.% of CMS3. In addition, the use of CMS as the pore former reduced the amount of closed pores in the anode disks from 2.05% to <1%.     

Eggshell nano-particle potential for methyl violet and mercury ion removal: Surface study and field application

A nano-scale sorbent was produced from eggshell wastes for sorption of Hg(II) and methyl violet (MV) from aqueous solutions and real wastewaters. The properties of the nano-particles were fully determined using SEM, DLS, FTIR, XRD, BET, TGA, AFM, EDAX, mapping, and TEM analyses. The adsorbent structure mainly contained carbonate and silica. The effects of influential parameters including temperature, contact time, initial contaminants concentration, sorbent dose, and initial pH on the removal efficiency were investigated. The maximum sorption efficiency of Hg(II) and MV occurred at pH of 6 and 9 and temperatures of 25 °C and 55 °C, respectively. Freundlich model could be interpreted the equilibrium data of the sorption process of both contaminants. The maximum sorption capacity of Hg(II) and MV using eggshell nano-particles was obtained as 116.27 mg/g and 123.45 mg/g, respectively. The dynamic behavior of the process was studied using two kinetic models. The sorption system performance was also examined and t_1/2 were determined as 4.34 min for Hg(II) and 4.97 min for MV. The sorption process of Hg(II) and MV was exothermic and endothermic, respectively. Effective sorption after seven cycles and successful treatment of landfill leachate and textile wastewater with eggshell nano-particles confirms its adequacy.     

A realistic multi-manned five-sided mixed-model assembly line balancing and scheduling problem with moving workers and limited workspace

The assembly line balancing problem can completely vary from one production line to the other. This paper deals with a realistic assembly line for the automotive industry inspired by Fiat Chrysler Automotive in North America and Parskhodro in Iran (both large-scale automotive companies). This problem includes some specific requirements that have not been studied in the literature. For example, the assembly line is five-sided, and workers can move along these sides. Due to the limited workspace, all the sides cannot work simultaneously at one station. First, a mixed integer linear programming model is proposed for the problem. Then, the model is improved to have a tighter linear relaxation. Moreover, an effective logic-based Benders’ decomposition algorithm is developed. After careful analysis of problem’s structure, three propositions are introduced. The master problem is well restricted by eight valid inequalities. Two different sub-problem types are defined to extract more information from the master problem’s solution. In this case, the algorithm adds effective cuts that reduce the solution space to the extent possible at each iteration. Thus, the number of iterations is significantly cut down. The performance of the model and algorithm, as well as improvement made on both, is evaluated.