The Experts below are selected from a list of 96 Experts worldwide ranked by ideXlab platform

A. Lygeros - One of the best experts on this subject based on the ideXlab platform.

  • Using hybrid neural networks in scaling up an FCC model from a pilot Plant to an industrial unit
    Chemical Engineering and Processing, 2003
    Co-Authors: George M. Bollas, S. Papadokonstadakis, J. Michalopoulos, George Arampatzis, Angelos A. Lappas, Iacovos A. Vasalos, A. Lygeros
    Abstract:

    The scaling up of a pilot Plant Fluid catalytic cracking (FCC) model to an industrial unit with use of artificial neural networks is presented in this paper. FCC is one of the most important oil refinery processes. Due to its complexity the modeling of the FCC poses great challenge. The pilot Plant model is capable of predicting the weight percent of conversion and coke yield of an FCC unit. This work is focused in determining the optimum hybrid approach, in order to improve the accuracy of the pilot Plant model. Industrial data from a Greek petroleum refinery were used to develop and validate the models. The hybrid models developed are compared with the pilot Plant model and a pure neural network model. The results show that the hybrid approach is able to increase the accuracy of prediction especially with data that is out of the model range. Furthermore, the hybrid models are easier to interpret and analyze.

  • Using hybrid neural networks in scaling up an FCC model from a pilot Plant to an industrial unit
    Chemical Engineering and Processing: Process Intensification, 2003
    Co-Authors: George M. Bollas, S. Papadokonstadakis, J. Michalopoulos, George Arampatzis, Angelos A. Lappas, Iacovos A. Vasalos, A. Lygeros
    Abstract:

    The scaling up of a pilot Plant Fluid catalytic cracking (FCC) model to an industrial unit with use of artificial neural networks is presented in this paper. FCC is one of the most important oil refinery processes. Due to its complexity the modeling of the FCC poses great challenge. The pilot Plant model is capable of predicting the weight percent of conversion and coke yield of an FCC unit. This work is focused in determining the optimum hybrid approach, in order to improve the accuracy of the pilot Plant model. Industrial data from a Greek petroleum refinery were used to develop and validate the models. The hybrid models developed are compared with the pilot Plant model and a pure neural network model. The results show that the hybrid approach is able to increase the accuracy of prediction especially with data that is out of the model range. Furthermore, the hybrid models are easier to interpret and analyze. (C) 2003 Elsevier Science B.V. All rights reserved

George M. Bollas - One of the best experts on this subject based on the ideXlab platform.

  • Using hybrid neural networks in scaling up an FCC model from a pilot Plant to an industrial unit
    Chemical Engineering and Processing, 2003
    Co-Authors: George M. Bollas, S. Papadokonstadakis, J. Michalopoulos, George Arampatzis, Angelos A. Lappas, Iacovos A. Vasalos, A. Lygeros
    Abstract:

    The scaling up of a pilot Plant Fluid catalytic cracking (FCC) model to an industrial unit with use of artificial neural networks is presented in this paper. FCC is one of the most important oil refinery processes. Due to its complexity the modeling of the FCC poses great challenge. The pilot Plant model is capable of predicting the weight percent of conversion and coke yield of an FCC unit. This work is focused in determining the optimum hybrid approach, in order to improve the accuracy of the pilot Plant model. Industrial data from a Greek petroleum refinery were used to develop and validate the models. The hybrid models developed are compared with the pilot Plant model and a pure neural network model. The results show that the hybrid approach is able to increase the accuracy of prediction especially with data that is out of the model range. Furthermore, the hybrid models are easier to interpret and analyze.

  • Using hybrid neural networks in scaling up an FCC model from a pilot Plant to an industrial unit
    Chemical Engineering and Processing: Process Intensification, 2003
    Co-Authors: George M. Bollas, S. Papadokonstadakis, J. Michalopoulos, George Arampatzis, Angelos A. Lappas, Iacovos A. Vasalos, A. Lygeros
    Abstract:

    The scaling up of a pilot Plant Fluid catalytic cracking (FCC) model to an industrial unit with use of artificial neural networks is presented in this paper. FCC is one of the most important oil refinery processes. Due to its complexity the modeling of the FCC poses great challenge. The pilot Plant model is capable of predicting the weight percent of conversion and coke yield of an FCC unit. This work is focused in determining the optimum hybrid approach, in order to improve the accuracy of the pilot Plant model. Industrial data from a Greek petroleum refinery were used to develop and validate the models. The hybrid models developed are compared with the pilot Plant model and a pure neural network model. The results show that the hybrid approach is able to increase the accuracy of prediction especially with data that is out of the model range. Furthermore, the hybrid models are easier to interpret and analyze. (C) 2003 Elsevier Science B.V. All rights reserved

Bryan C. Carstens - One of the best experts on this subject based on the ideXlab platform.

  • Biogeographic barriers drive co-diversification within associated eukaryotes of the Sarracenia alata pitcher Plant system.
    PeerJ, 2016
    Co-Authors: Jordan D. Satler, Amanda J. Zellmer, Bryan C. Carstens
    Abstract:

    Understanding if the members of an ecological community have co-diversified is a central concern of evolutionary biology, as co-diversification suggests prolonged association and possible coevolution. By sampling associated species from an ecosystem, researchers can better understand how abiotic and biotic factors influence diversification in a region. In particular, studies of co-distributed species that interact ecologically can allow us to disentangle the effect of how historical processes have helped shape community level structure and interactions. Here we investigate the Sarracenia alata pitcher Plant system, an ecological community where many species from disparate taxonomic groups live inside the Fluid-filled pitcher leaves. Direct sequencing of the eukaryotes present in the pitcher Plant Fluid enables us to better understand how a host Plant can shape and contribute to the genetic structure of its associated inquilines, and to ask whether genetic variation in the taxa are structured in a similar manner to the host Plant. We used 454 amplicon-based metagenomics to demonstrate that the pattern of genetic diversity in many, but not all, of the eukaryotic community is similar to that of S. alata, providing evidence that associated eukaryotes share an evolutionary history with the host pitcher Plant. Our work provides further evidence that a host Plant can influence the evolution of its associated commensals.

  • The Carnivorous Pale Pitcher Plant Harbors Diverse, Distinct, and Time-Dependent Bacterial Communities
    Applied and environmental microbiology, 2010
    Co-Authors: Margaret M. Koopman, Danielle M. Fuselier, Sarah M. Hird, Bryan C. Carstens
    Abstract:

    The ability of American carnivorous pitcher Plants (Sarracenia) to digest insect prey is facilitated by microbial associations. Knowledge of the details surrounding this interaction has been limited by our capability to characterize bacterial diversity in this system. To describe microbial diversity within and between pitchers of one species, Sarracenia alata, and to explore how these communities change over time as pitchers accumulate and digest insect prey, we collected and analyzed environmental sequence tag (454 pyrosequencing) and genomic fingerprint (automated ribosomal intergenic spacer analysis and terminal restriction fragment length polymorphism) data. Microbial richness associated with pitcher Plant Fluid is high; more than 1,000 unique phylogroups were identified across at least seven phyla and 50 families. We documented an increase in bacterial diversity and abundance with time and observed repeated changes in bacterial community composition. Pitchers from different Plants harbored significantly more similar bacterial communities at a given time point than communities coming from the same genetic host over time. The microbial communities in pitcher Plant Fluid also differ significantly from those present in the surrounding soil. These findings indicate that the bacteria associated with pitcher Plant leaves are far from random assemblages and represent an important step toward understanding this unique Plant-microbe interaction.

George Arampatzis - One of the best experts on this subject based on the ideXlab platform.

  • Using hybrid neural networks in scaling up an FCC model from a pilot Plant to an industrial unit
    Chemical Engineering and Processing, 2003
    Co-Authors: George M. Bollas, S. Papadokonstadakis, J. Michalopoulos, George Arampatzis, Angelos A. Lappas, Iacovos A. Vasalos, A. Lygeros
    Abstract:

    The scaling up of a pilot Plant Fluid catalytic cracking (FCC) model to an industrial unit with use of artificial neural networks is presented in this paper. FCC is one of the most important oil refinery processes. Due to its complexity the modeling of the FCC poses great challenge. The pilot Plant model is capable of predicting the weight percent of conversion and coke yield of an FCC unit. This work is focused in determining the optimum hybrid approach, in order to improve the accuracy of the pilot Plant model. Industrial data from a Greek petroleum refinery were used to develop and validate the models. The hybrid models developed are compared with the pilot Plant model and a pure neural network model. The results show that the hybrid approach is able to increase the accuracy of prediction especially with data that is out of the model range. Furthermore, the hybrid models are easier to interpret and analyze.

  • Using hybrid neural networks in scaling up an FCC model from a pilot Plant to an industrial unit
    Chemical Engineering and Processing: Process Intensification, 2003
    Co-Authors: George M. Bollas, S. Papadokonstadakis, J. Michalopoulos, George Arampatzis, Angelos A. Lappas, Iacovos A. Vasalos, A. Lygeros
    Abstract:

    The scaling up of a pilot Plant Fluid catalytic cracking (FCC) model to an industrial unit with use of artificial neural networks is presented in this paper. FCC is one of the most important oil refinery processes. Due to its complexity the modeling of the FCC poses great challenge. The pilot Plant model is capable of predicting the weight percent of conversion and coke yield of an FCC unit. This work is focused in determining the optimum hybrid approach, in order to improve the accuracy of the pilot Plant model. Industrial data from a Greek petroleum refinery were used to develop and validate the models. The hybrid models developed are compared with the pilot Plant model and a pure neural network model. The results show that the hybrid approach is able to increase the accuracy of prediction especially with data that is out of the model range. Furthermore, the hybrid models are easier to interpret and analyze. (C) 2003 Elsevier Science B.V. All rights reserved

Iacovos A. Vasalos - One of the best experts on this subject based on the ideXlab platform.

  • Using hybrid neural networks in scaling up an FCC model from a pilot Plant to an industrial unit
    Chemical Engineering and Processing, 2003
    Co-Authors: George M. Bollas, S. Papadokonstadakis, J. Michalopoulos, George Arampatzis, Angelos A. Lappas, Iacovos A. Vasalos, A. Lygeros
    Abstract:

    The scaling up of a pilot Plant Fluid catalytic cracking (FCC) model to an industrial unit with use of artificial neural networks is presented in this paper. FCC is one of the most important oil refinery processes. Due to its complexity the modeling of the FCC poses great challenge. The pilot Plant model is capable of predicting the weight percent of conversion and coke yield of an FCC unit. This work is focused in determining the optimum hybrid approach, in order to improve the accuracy of the pilot Plant model. Industrial data from a Greek petroleum refinery were used to develop and validate the models. The hybrid models developed are compared with the pilot Plant model and a pure neural network model. The results show that the hybrid approach is able to increase the accuracy of prediction especially with data that is out of the model range. Furthermore, the hybrid models are easier to interpret and analyze.

  • Using hybrid neural networks in scaling up an FCC model from a pilot Plant to an industrial unit
    Chemical Engineering and Processing: Process Intensification, 2003
    Co-Authors: George M. Bollas, S. Papadokonstadakis, J. Michalopoulos, George Arampatzis, Angelos A. Lappas, Iacovos A. Vasalos, A. Lygeros
    Abstract:

    The scaling up of a pilot Plant Fluid catalytic cracking (FCC) model to an industrial unit with use of artificial neural networks is presented in this paper. FCC is one of the most important oil refinery processes. Due to its complexity the modeling of the FCC poses great challenge. The pilot Plant model is capable of predicting the weight percent of conversion and coke yield of an FCC unit. This work is focused in determining the optimum hybrid approach, in order to improve the accuracy of the pilot Plant model. Industrial data from a Greek petroleum refinery were used to develop and validate the models. The hybrid models developed are compared with the pilot Plant model and a pure neural network model. The results show that the hybrid approach is able to increase the accuracy of prediction especially with data that is out of the model range. Furthermore, the hybrid models are easier to interpret and analyze. (C) 2003 Elsevier Science B.V. All rights reserved