Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production

In this study, various Artificial Neural Networks (ANNs) were developed to estimate the production yield of greenhouse basil in Iran. For this purpose, the data collected by random method from 26 greenhouses in the region during four periods of plant cultivation in 2009–2010. The total input energy and energy ratio for basil production were 14,308,998 MJ ha^?1 and 0.02, respectively. The developed ANN was a multilayer perceptron (MLP) with seven neurons in the input layer, one, two and three hidden layer(s) of various numbers of neurons and one neuron (basil yield) in the output layer. The input energies were human labor, diesel fuel, chemical fertilizers, farm yard manure, chemicals, electricity and transportation. Results showed, the ANN model having 7-20-20-1 topology can predict the yield value with higher accuracy. So, this two hidden layer topology was selected as the best model for estimating basil production of regional greenhouses with similar conditions. For the optimal model, the values of the models outputs correlated well with actual outputs, with coefficient of determination (R²) of 0.976. For this configuration, RMSE and MAE values were 0.046 and 0.035, respectively. Sensitivity analysis revealed that chemical fertilizers are the most significant parameter in the basil production.     

Process synthesis and optimization of biorefinery configurations

Recently, there has been a growing interest in the development of cost‐effective technologies for the production of biofuels. A common approach to biofuel research is to invent or improve a biochemical or thermochemical conversion step. Subsequently, other conversion and separation steps are added to form a complete biorefinery flowsheet. Because this approach is structured around a specific conversion step, it may limit the possibilities of configuring optimal and innovative biorefineries. This article proposes a novel and systematic two‐stage approach to the synthesis and optimization of biorefinery configurations, given available feedstocks and desired products. In the synthesis stage, a systems‐based approach is developed to create a methodical way for synthesizing integrated biorefineries. This method is referred to as “forward‐backward” approach. It involves forward synthesis of biomass to possible intermediates and reverse synthesis starting with the desired products and identifying necessary species and pathways leading to them. In the optimization stage, Bellman's principle of optimality is applied to decompose the optimization problem into subproblems in which an optimal policy of available technologies is determined for every conversion step. An optimization formulation is utilized to determine the optimal configuration based on screening and connecting the optimal policies and generating the biorefinery flowsheet. A case study of alcohol‐producing pathways from lignocellulosic biomass is solved to demonstrate the merits of the proposed approach. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

A combined physicochemical and electrocatalytic study of microwave synthesized tungsten mono-carbide nanoparticles on multiwalled carbon nanotubes as a co-catalyst for a proton-exchange membrane fuel cell

Tungsten mono-carbide (WC) nanoparticles supported on multiwalled carbon nanotube (MWCNT) was synthesized by a microwave-assisted solid-state carburization. The prepared samples were used as a co-catalyst to prepare Pt-WC/MWCNT catalyst for a proton-exchange membrane fuel cell. MWCNTs with and without oxidative pretreatments were characterized as the starting precursors. The influence of the carbide formation conditions on the physicochemical characteristics of the final product were extensively investigated. According to the results, surface pretreatment of the MWCNTs can improve the yield of carbide formation. Furthermore, carburization process can improve the catalyst utilization due to increasing the number of surface defects of the MWCNT supporting materials which can be interpreted as structural effect of the carburization process. It is believed that the superior performance of electrodes modified with tungsten carbide is mostly due to the structural effect of the carburization process and synergistic effect between the electrocatalytic activity of WC and Pt.     

Impacts of user-selfishness on cooperative content caching in social wireless networks

Cooperative caching can be an effective mechanism for reducing electronic content provisioning cost in Social Wireless Networks (SWNETs). These networks are formed by a collection of mobile data enabled devices physically gathering in settings such as university campus, malls, airport and other public places. In this paper, we first propose an optimal collaborative object caching policy in order to minimize the object provisioning cost in SWNETs with homogenous user requests and a peer-rebate model for promoting collaboration. Then using an analytical model we study the impacts of user selfishness on the provisioning cost and the earned rebate when certain nodes in an SWNET selfishly deviate from the optimal policy. User selfishness is motivated in order to either increase individually earned rebate or to reduce content provisioning cost. The analytical model is validated by experimental results from simulated SWNETs using the network simulator ns2.     

A multi-variate heart disease optimization and recognition framework

Neural Computing and Applications - Cardiovascular diseases (CVD) are the most widely spread diseases all over the world among the common chronic diseases. CVD represents one of the main causes of...     

Influence of surface energy on the nanoindentation response of elastically-layered viscoelastic materials

In the context of integrated nonlinear viscoelastic contact mechanics, a nonlinear finite element model is developed to predict and analyze the quasistatic response of nanoindentation problems of an elastically-layered viscoelastic materials considering the surface elasticity effects. Effects of surface energy are accounted for by employing the Gurtin–Murdoch continuum model for surface elasticity. The linear viscoelastic response is modeled by the Schapery’s creep model with a Prony’s series to express the transient component in the creep compliance. The viscoelastic constitutive equations are cast into a recursive form that needs only the previous time increment rather than the entire strain history. To satisfy the contact constraints exactly, the Lagrange multiplier method is adopted to enforce the contact conditions into the system. The equilibrium indentation configuration is obtained through the Newton–Raphson iterative procedure. The developed model is verified then applied to investigate the quasistatic nanoindentation response of two different indentation problems with different geometry and loading conditions. Results show the significant effects of surface energy and viscoelasticity on the quasistatic nanoindentation response.