The Experts below are selected from a list of 64023 Experts worldwide ranked by ideXlab platform
Christos T Maravelias - One of the best experts on this subject based on the ideXlab platform.
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simultaneous chemical process synthesis and heat integration with unclassified hot cold process streams
Computers & Chemical Engineering, 2017Co-Authors: Lingxun Kong, Venkatachalam Avadiappan, Kefeng Huang, Christos T MaraveliasAbstract:Abstract We propose a mixed-integer nonlinear programming (MINLP) model for the simultaneous chemical process synthesis and heat integration with unclassified process streams. The model accounts for (1) streams that cannot be classified as hot or cold, and (2) Variable stream temperatures and flow rates, thereby facilitating integration with a process synthesis model. The hot/cold stream “identities” are represented by classification Binary Variables which are (de)activated based on the relative stream inlet and outlet temperatures. Variables including stream temperatures and heat loads are disaggregated into hot and cold Variables, and each Variable is (de)activated by the corresponding classification Binary Variable. Stream inlet/outlet temperatures are positioned onto “dynamic” temperature intervals so that heat loads at each interval can be properly calculated. The proposed model is applied to two illustrative examples with Variable stream flow rates and temperatures, and is integrated with a superstructure-based process synthesis model to illustrate its applicability.
Linda L Wright - One of the best experts on this subject based on the ideXlab platform.
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a simulation based technique to estimate intracluster correlation for a Binary Variable
Contemporary Clinical Trials, 2009Co-Authors: Hrishikesh Chakraborty, Janet Moore, Waldemar A Carlo, Tyler Hartwell, Linda L WrightAbstract:Cluster randomized trials have become the design of choice for evaluating the effect of selected interventions on well-known health indicators such as neonatal mortality rate, episiotomy rate, and postpartum hemorrhage rate in a community setting. Determining the sample size of a cluster randomized trial requires a reliable estimate of cluster size and the intracluster correlation (ICC), because sample size can be substantially impacted by these parameters. During the design phase of a trial, the investigators may have estimates of the valid range of the health indicator which is the primary outcome Variable. Furthermore, investigators often have an estimate of the average cluster size or range of cluster sizes that exist among the proposed samples they are planning to include in the trial. We present in this article a simulation technique to estimate the ICC value and its distribution for known Binary outcome Variables and a varying number of clusters and cluster sizes. We applied this technique to estimate ICC values and confidence intervals for a multi-country trial assessing the effect of neonatal resuscitation to decrease seven-day neonatal mortality, where communities within a country were clusters. This simulation technique can be used to estimate the possible ranges of the ICC values and to help to design an appropriately powered trial.
Jinli Zhang - One of the best experts on this subject based on the ideXlab platform.
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simultaneous heat exchanger network synthesis involving nonisothermal mixing streams with temperature dependent heat capacity
Industrial & Engineering Chemistry Research, 2015Co-Authors: Hao Wu, Wei Li, Jinli ZhangAbstract:Heat exchanger network synthesis (HENS) is faced with the dilemma of substantial nonisothermal mixing and nonconstant thermal properties of streams against the higher computational efficiency required to solve mathematical programming models with a large amount of Variables and high nonlinearity. In this work, by replacing the conventional Binary Variable in a HENS model, a nonlinear approximation term is developed to indicate the existence of a heat exchanger so as to reduce the amount of Variables and constraint as well as enhance the model’s computational efficiency. With this approximation, a new HENS model was formulated considering simultaneously nonisothermal mixing and temperature-dependent heat capacity flow rate. All the Variables of the model are stated with clear upper and lower bounds. A 10-stream (5 hot process streams and 5 cold process streams) benchmark HENS problem is solved by the developed model. The results show that the proposed model can generate a better cost-optimal heat exchanger...
Lingxun Kong - One of the best experts on this subject based on the ideXlab platform.
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simultaneous chemical process synthesis and heat integration with unclassified hot cold process streams
Computers & Chemical Engineering, 2017Co-Authors: Lingxun Kong, Venkatachalam Avadiappan, Kefeng Huang, Christos T MaraveliasAbstract:Abstract We propose a mixed-integer nonlinear programming (MINLP) model for the simultaneous chemical process synthesis and heat integration with unclassified process streams. The model accounts for (1) streams that cannot be classified as hot or cold, and (2) Variable stream temperatures and flow rates, thereby facilitating integration with a process synthesis model. The hot/cold stream “identities” are represented by classification Binary Variables which are (de)activated based on the relative stream inlet and outlet temperatures. Variables including stream temperatures and heat loads are disaggregated into hot and cold Variables, and each Variable is (de)activated by the corresponding classification Binary Variable. Stream inlet/outlet temperatures are positioned onto “dynamic” temperature intervals so that heat loads at each interval can be properly calculated. The proposed model is applied to two illustrative examples with Variable stream flow rates and temperatures, and is integrated with a superstructure-based process synthesis model to illustrate its applicability.
Hrishikesh Chakraborty - One of the best experts on this subject based on the ideXlab platform.
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a simulation based technique to estimate intracluster correlation for a Binary Variable
Contemporary Clinical Trials, 2009Co-Authors: Hrishikesh Chakraborty, Janet Moore, Waldemar A Carlo, Tyler Hartwell, Linda L WrightAbstract:Cluster randomized trials have become the design of choice for evaluating the effect of selected interventions on well-known health indicators such as neonatal mortality rate, episiotomy rate, and postpartum hemorrhage rate in a community setting. Determining the sample size of a cluster randomized trial requires a reliable estimate of cluster size and the intracluster correlation (ICC), because sample size can be substantially impacted by these parameters. During the design phase of a trial, the investigators may have estimates of the valid range of the health indicator which is the primary outcome Variable. Furthermore, investigators often have an estimate of the average cluster size or range of cluster sizes that exist among the proposed samples they are planning to include in the trial. We present in this article a simulation technique to estimate the ICC value and its distribution for known Binary outcome Variables and a varying number of clusters and cluster sizes. We applied this technique to estimate ICC values and confidence intervals for a multi-country trial assessing the effect of neonatal resuscitation to decrease seven-day neonatal mortality, where communities within a country were clusters. This simulation technique can be used to estimate the possible ranges of the ICC values and to help to design an appropriately powered trial.