Dynamic thermodynamic flux balance analysis and life cycle analysis of microbial biofuels

Recently, a paradigm shift from fossil fuel energy to renewable energy is observed because of the environmental awareness. Algae are abundant, carbon neutral and renewable, which make them high potential materials to be developed as a fuel source, pending economic constraints. If algae are used as a platform, a clear and precise insight to the pathway should be presented. This requires a deep understanding of gene-protein-reaction systems. Using the genome-scale metabolic networks, a better description of the cellular metabolism and strain optimization will be attained; this will help to decrease the demand for expensive in-vivo experiments. First Phase: one major objective of this research was to maximize the production rate of algae biofuel at different process conditions with economic and environmental considerations. We did a comprehensive review on life cycle analysis of algal biodiesel. In this review, the effect of different process variables on the environmental impacts of algal biodiesel in the literature were systematically presented. Second Phase: We integrated the biological data and thermodynamic constraints to establish a realistic metabolic phenotypic space. With the aid of public metabolic networks, the MODEL SEED database, and component contribution, we incorporated the thermodynamics and chemical reactions constraints. Third Phase: In metabolic network modeling, many simulations carry out in “static” state whereas our interest is to predict the behavior in a “dynamical” approach and to understand how environment and intracellular interact. In addition, the metabolic phenotype of cell systems often involves high levels of nutrient uptake and excessive byproduct secretion. In silico scenarios were used to simulate diauxic growth under two different situations. The glucose and xylose as main component of lignocellulosic biomass defined in media and allow E. coli to grow on them. Then under fully aerobic condition and later of under aerobic to anaerobic transition, simulations were performed to see how our proposed dynamic thermodynamic flux balance (DT-FBA) captures cell behaviors

Regressive approach for predicting bearing capacity of bored piles from cone penetration test data

© 2015 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. In this study, the least square support vector machine (LSSVM) algorithm was applied to predicting the bearing capacity of bored piles embedded in sand and mixed soils. Pile geometry and cone penetration test (CPT) results were used as input variables for prediction of pile bearing capacity. The data used were collected from the existing literature and consisted of 50 case records. The application of LSSVM was carried out by dividing the data into three sets: a training set for learning the problem and obtaining a relationship between input variables and pile bearing capacity, and testing and validation sets for evaluation of the predictive and generalization ability of the obtained relationship. The predictions of pile bearing capacity by LSSVM were evaluated by comparing with experimental data and with those by traditional CPT-based methods and the gene expression programming (GEP) model. It was found that the LSSVM performs well with coefficient of determination, mean, and standard deviation equivalent to 0.99, 1.03, and 0.08, respectively, for the testing set, and 1, 1.04, and 0.11, respectively, for the validation set. The low values of the calculated mean squared error and mean absolute error indicated that the LSSVM was accurate in predicting the pile bearing capacity. The results of comparison also showed that the proposed algorithm predicted the pile bearing capacity more accurately than the traditional methods including the GEP model

ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network

Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based Internet infrastructure. However, NDN is subject to congestion when the number of data packets that reach one or various routers in a certain period of time is so high than its queue gets overflowed. To address this problem many congestion control protocols have been proposed in the literature which, however, they are highly sensitive to their control parameters as well as unable to predict congestion traffic well enough in advance. This paper develops an Adaptive Congestion Control Protocol in NDN (ACCPndn) by learning capacities in two phases to control congestion traffics before they start impacting the network performance. In the first phase – adaptive training – we propose a Time-Lagged Feedforward Network (TLFN) optimized by hybridization of particle swarm optimization and genetic algorithm to predict the source of congestion together with the amount of congestion. In the second phase -fuzzy avoidance- we employ a non-linear fuzzy logic-based control system to make a proactive decision based on the outcomes of first phase in each router per interface to control and/or prevent packet drop well enough in advance. Extensive simulations and results show that ACCPndn sufficiently satisfies the applied performance metrics and outperforms two previous proposals such as NACK and HoBHIS in terms of the minimal packet drop and high-utilization (retrying alternative paths) in bottleneck links to mitigate congestion traffics

The Application of Least Square Support Vector Machine as a Mathematical Algorithm for Diagnosing Drilling Effectivity in Shaly Formations

The problem of slow drilling in deep shale formations occurs worldwide causing significant expenses to the oil industry. Bit balling which is widely considered as the main cause of poor bit performance in shales, especially deep shales, is being drilled with water-based mud. Therefore, efforts have been made to develop a model to diagnose drilling effectivity. Hence, we arrived at graphical correlations which utilized the rate of penetration, depth of cut, specific energy, and cation exchange capacity in order to provide a tool for the prediction of drilling classes.This paper describes a robust support vector regression (SVR) methodology that offers superior performance for important drilling engineering problems. Using the amount of cation exchange capacity of the shaly formations and correlating them to drilling parameters such as the normalized rate of penetration, depth of cut, and specific energy, the model was developed. The method incorporates hybrid least square support vector regression into the coupled simulated annealing (CSA) optimization technique (LSSVM-CSA) for the efficient tuning of SVR hyper parameters. Also, we performed receiver operating characteristic as a performance indicator used for the evaluation of classifiers. The performance analysis shows that LSSVM classifier noticeably performs with high accuracy, and adapting such intelligence system will help petroleum industries deal with the well drilling consciously.The problem of slow drilling in deep shale formations occur worldwide causing significant expense to the oil industry. Bit balling is widely considered as the main cause of poor bit performance in shale, especially deep shale are being drilled withwater-based mud .Therefore, efforts have been made to develop a model to diagnose drilling ineffectivity/effectivity. Hencewe arrived to graphical correlations which utilized rate of penetration, depth of cut, specific energy, and cation exchange capacity in order to provide a tool for prediction of drilling classes.This paper describes a robust support vector regression (SVR) methodology that offers superior performance for important drilling engineering problems. Using the amount of cation exchange capacity of the shaly formations and also correlating themto drilling parameters, such as normalized rate of penetration, depth of cut, and specific energy, model was developed. Themethod incorporates hybrid least square support vector regression and Coupled Simulated Annealing (CSA) optimization technique (LSSVM-CSA) for efficient tuning of SVR hyper parameters. Also, we performed Receiver Operating Characteristic as a performance indicator which used for evaluation of classifiers. Performance analysis shows that LSSVM classifier noticeably perform with high accuracy and adapting such intelligence system will help petroleum industry to dealing the well drilling consciously

Integration of LSSVM technique with PSO to determine asphaltene deposition

Asphaltene deposition is a recognized phenomenon in petroleum industry with undesirable outcomes so that it may lead to wellbore plugging and formation damage, resulting in a large amount of remedial costs to decrease its negative impacts on oil production. Therefore, it has attracted lots of research interests in the literature. In this study, an attempt is made to introduce the least square support vector machine (LSSVM) for prediction of asphaltene deposition. This technique with high capabilities which captures the complex nature of asphaltene could be inferred as a scaling model. As there is no a standard procedure to determine the main parameters of the LSSVM model, the particle swarm optimization (PSO) technique is employed to synchronously optimize the LSSVM parameters. The modeling results clearly demonstrate that the optimized LSSVM is able to handle the nonlinearities well and attain satisfactory results. The comparison of available predictive equations for asphaltene deposition confirms that the LSSVM technique linked with PSO exhibits higher robustness and greater precision with an R2 of 0.989 for the testing phase