The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform

Prabal Talukdar - One of the best experts on this subject based on the ideXlab platform.

  • performance evaluation of two heat transfer models of a walking beam type Reheat Furnace
    Heat Transfer Engineering, 2015
    Co-Authors: Vinod Kumar Singh, Prabal Talukdar, P J Coelho
    Abstract:

    Two different heat transfer models for predicting the transient heat transfer characteristics of the slabs in a walking beam type Reheat Furnace are compared in this work. The prediction of heat flux on the slab surface and the temperature distribution inside the slab have been determined by considering thermal radiation in the Furnace chamber and transient heat conduction in the slab. Both models have been compared for their accuracy and computational time. The Furnace is modeled as an enclosure with a radiatively participating medium. In the first model, the three-dimensional (3D) transient heat conduction equation with a radiative heat flux boundary condition is solved using an in-house code. The radiative heat flux incident on the slab surface required in the boundary condition of the conduction code is calculated using the commercial software FLUENT. The second model uses entirely FLUENT along with a user-defined function, which has been developed to account for the movement of slabs. The results obt...

  • comparisons of different heat transfer models of a walking beam type Reheat Furnace
    International Communications in Heat and Mass Transfer, 2013
    Co-Authors: Vinod Kumar Singh, Prabal Talukdar
    Abstract:

    Abstract Four different heat transfer models (Model-1 to -4) for the prediction of temperature of the slabs of a walking beam type Reheat Furnace have been compared. The models are classified based on the solution methodology and simplifications. In the first three models (Model-1 to -3), the Furnace is modelled as radiating medium with spatially varying known temperature. Model-1 solves the 3D transient conduction in the slab and radiation in the Furnace separately and is coupled via the boundary condition. In the second model, both radiation in the Furnace and conduction in the slab are solved simultaneously. A user defined function (UDF) programme has been developed to process the movement of the slabs. Model-3 is similar to Model-2 but it includes additionally the skid support systems for the slabs. In the Model-4, convection in the Furnace has been included in addition to all the features considered in Model-3. The convection has been modelled with the consideration of flow of hot gas through the inlet of the burners. All the models have been compared for their performance and computational time. Model-1 has been found to be quite economical and accurate. The inclusion of skid supporting system has little effect in the temperature distribution in the slab.

Kurtis Calder - One of the best experts on this subject based on the ideXlab platform.

  • energy efficiency assessment by process heating assessment and survey tool phast and feasibility analysis of waste heat recovery in the Reheat Furnace at a steel company
    Renewable & Sustainable Energy Reviews, 2011
    Co-Authors: Minxing Si, Shirley Thompson, Kurtis Calder
    Abstract:

    The steel industry is one of the most energy intensive industries, contributing greenhouse gas (GHG) emissions. This research analyzes the feasibility of waste heat recovery and assesses energy efficiency at a steel company, Gerdau Ameristeel in Selkirk, Manitoba. The process heating assessment and survey tool (PHAST) determined that the overall efficiency in the Reheat Furnace is 60%. Flue gas losses are the biggest energy losses in the Reheat Furnace, accounting for 29.5% of the total energy losses during full production. Heat losses from wall, hearth and roof are also significant, being 7,139,170 kJ/h during full production. To reduce energy inefficiencies, it is recommended that billets be pReheated to 315 °C in the Reheat Furnace. This requires 1.48 h to capture waste heat with a pReheating section length of 1691.64 cm. The annual energy savings are estimated to be $215,086.12 requiring a 3.03 years payback period. This study was the first to determine the required size of a pReheating box and the rate of heat transfer through billets in the pReheating section.

Vinod Kumar Singh - One of the best experts on this subject based on the ideXlab platform.

  • performance evaluation of two heat transfer models of a walking beam type Reheat Furnace
    Heat Transfer Engineering, 2015
    Co-Authors: Vinod Kumar Singh, Prabal Talukdar, P J Coelho
    Abstract:

    Two different heat transfer models for predicting the transient heat transfer characteristics of the slabs in a walking beam type Reheat Furnace are compared in this work. The prediction of heat flux on the slab surface and the temperature distribution inside the slab have been determined by considering thermal radiation in the Furnace chamber and transient heat conduction in the slab. Both models have been compared for their accuracy and computational time. The Furnace is modeled as an enclosure with a radiatively participating medium. In the first model, the three-dimensional (3D) transient heat conduction equation with a radiative heat flux boundary condition is solved using an in-house code. The radiative heat flux incident on the slab surface required in the boundary condition of the conduction code is calculated using the commercial software FLUENT. The second model uses entirely FLUENT along with a user-defined function, which has been developed to account for the movement of slabs. The results obt...

  • comparisons of different heat transfer models of a walking beam type Reheat Furnace
    International Communications in Heat and Mass Transfer, 2013
    Co-Authors: Vinod Kumar Singh, Prabal Talukdar
    Abstract:

    Abstract Four different heat transfer models (Model-1 to -4) for the prediction of temperature of the slabs of a walking beam type Reheat Furnace have been compared. The models are classified based on the solution methodology and simplifications. In the first three models (Model-1 to -3), the Furnace is modelled as radiating medium with spatially varying known temperature. Model-1 solves the 3D transient conduction in the slab and radiation in the Furnace separately and is coupled via the boundary condition. In the second model, both radiation in the Furnace and conduction in the slab are solved simultaneously. A user defined function (UDF) programme has been developed to process the movement of the slabs. Model-3 is similar to Model-2 but it includes additionally the skid support systems for the slabs. In the Model-4, convection in the Furnace has been included in addition to all the features considered in Model-3. The convection has been modelled with the consideration of flow of hot gas through the inlet of the burners. All the models have been compared for their performance and computational time. Model-1 has been found to be quite economical and accurate. The inclusion of skid supporting system has little effect in the temperature distribution in the slab.

Minxing Si - One of the best experts on this subject based on the ideXlab platform.

  • energy efficiency assessment by process heating assessment and survey tool phast and feasibility analysis of waste heat recovery in the Reheat Furnace at a steel company
    Renewable & Sustainable Energy Reviews, 2011
    Co-Authors: Minxing Si, Shirley Thompson, Kurtis Calder
    Abstract:

    The steel industry is one of the most energy intensive industries, contributing greenhouse gas (GHG) emissions. This research analyzes the feasibility of waste heat recovery and assesses energy efficiency at a steel company, Gerdau Ameristeel in Selkirk, Manitoba. The process heating assessment and survey tool (PHAST) determined that the overall efficiency in the Reheat Furnace is 60%. Flue gas losses are the biggest energy losses in the Reheat Furnace, accounting for 29.5% of the total energy losses during full production. Heat losses from wall, hearth and roof are also significant, being 7,139,170 kJ/h during full production. To reduce energy inefficiencies, it is recommended that billets be pReheated to 315 °C in the Reheat Furnace. This requires 1.48 h to capture waste heat with a pReheating section length of 1691.64 cm. The annual energy savings are estimated to be $215,086.12 requiring a 3.03 years payback period. This study was the first to determine the required size of a pReheating box and the rate of heat transfer through billets in the pReheating section.

Maysam F Abbod - One of the best experts on this subject based on the ideXlab platform.

  • Multi-Agent System for Dynamic Manufacturing System Optimization
    2014
    Co-Authors: Tawfeeq Al-kanhal, Maysam F Abbod
    Abstract:

    Abstract. This paper deals with the application of multi-agent system concept for optimization of dynamic uncertain process. These problems are known to have a computationally demanding objective function, which could turn to be infeasible when large problems are considered. Therefore, fast approximations to the objective function are required. This paper employs bundle of intelligent systems algorithms tied together in a multi-agent system. In order to demonstrate the system, a metal Reheat Furnace scheduling problem is adopted for highly demanded optimization problem. The proposed multi-agent approach has been evaluated for different settings of the Reheat Furnace scheduling problem. Particle Swarm Optimization, Genetic Algorithm with different classic and advanced versions: GA with chromosome differentiation, Age GA, and Sexual GA, and finally a Mimetic GA, which is based on combining the GA as a global optimizer and the PSO as a local optimizer. Experimentation has been performed to validate the multi-agent system on the Reheat Furnace scheduling problem. Key words: GA; PSO; multi-agent system; Reheat Furnace; scheduling.

  • modelling and optimisation of Reheat Furnace
    European Symposium on Computer Modeling and Simulation, 2008
    Co-Authors: Tawfeeq Abdullah Alkanhal, Maysam F Abbod
    Abstract:

    Some problems are known to have computationally demanding objective function, which could turn to be infeasible when large problems are considered. Therefore, fast approximations to the objective function are required. This paper employs portfolio of intelligent systems algorithms for optimising a metal Reheat Furnace scheduling problem. The proposed system has been evaluated for different techniques of the Reheat Furnace scheduling problem. Different optimisation methods have been used, namely: particle swarm optimisation (PSO), genetic algorithm (GA) with different classic and advanced versions: GA with chromosome differentiation (GACD), age GA (AGA), and sexual GA (SGA), and finally a mimetic GA (MGA), which is based on combining the GA as a global optimiser and the PSO as a local optimiser. Simulations have been performed to evaluate the systempsilas performance.

  • Multi-agent system for dynamic manufacturing system optimization
    'Springer Fachmedien Wiesbaden GmbH', 2008
    Co-Authors: Al-kanhal T, Maysam F Abbod
    Abstract:

    This paper deals with the application of multi-agent system concept for optimization of dynamic uncertain process. These problems are known to have a computationally demanding objective function, which could turn to be infeasible when large problems are considered. Therefore, fast approximations to the objective function are required. This paper employs bundle of intelligent systems algorithms tied together in a multi-agent system. In order to demonstrate the system, a metal Reheat Furnace scheduling problem is adopted for highly demanded optimization problem. The proposed multi-agent approach has been evaluated for different settings of the Reheat Furnace scheduling problem. Particle Swarm Optimization, Genetic Algorithm with different classic and advanced versions: GA with chromosome differentiation, Age GA, and Sexual GA, and finally a Mimetic GA, which is based on combining the GA as a global optimizer and the PSO as a local optimizer. Experimentation has been performed to validate the multi-agent system on the Reheat Furnace scheduling problem