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Abstract Many countries committed themselves in the Kyoto protocol to reduce greenhouse gas (GHG) emissions. Some of these targeted emission reductions could result from a switch from coal-fired to gas-fired electricity generation. The focus in this work lies on Western Europe, with the presence of the European Union Emission Trading Scheme (EU ETS). For the switching to occur, several conditions have to be fulfilled. First, an economical incentive must be present, i.e. a sufficiently high European Union Allowance (EUA) price together with a sufficiently low natural gas price. Second, the physical potential for switching must exist, i.e. at a given load, there must remain enough power plants not running to make switching possible. This paper investigates what possibilities exist for switching coal-fired plants for gas-fired plants, dependent on the load level (the latter condition above). A fixed allowance cost and a variable natural gas price are assumed. The method to address GHG emission reduction potentials is first illustrated in a methodological case. Next, the GHG emission reduction potentials are addressed for several Western European countries together with a relative positioning of their electricity generation. GHG emission reduction potentials are also compared with simulation results. GHG emission reduction potentials tend to be significant. The Netherlands have a very widespread switching zone, so GHG emission reduction is practically independent of electricity generation. Other counties, like Germany, Spain and Italy could reduce GHG emissions significantly by switching. With an allowance cost following the switch level of a 50% efficient gas-fired plant and a 40% efficient coal-fired plant in the summer season (like in 2005), the global GHG emission reduction (in the electricity generating sector) for the eight modeled zones could amount to 19%.

Abstract Many countries committed themselves in the Kyoto protocol to reduce greenhouse gas (GHG) emissions. Some of these targeted emission reductions could result from a switch from coal-fired to gas-fired electricity generation. The focus in this work lies on Western Europe, with the presence of the European Union Emission Trading Scheme (EU ETS). For the switching to occur, several conditions have to be fulfilled. First, an economical incentive must be present, i.e. a sufficiently high European Union Allowance (EUA) price together with a sufficiently low natural gas price. Second, the physical potential for switching must exist, i.e. at a given load, there must remain enough power plants not running to make switching possible. This paper investigates what possibilities exist for switching coal-fired plants for gas-fired plants, dependent on the load level (the latter condition above). A fixed allowance cost and a variable natural gas price are assumed. The method to address GHG emission reduction potentials is first illustrated in a methodological case. Next, the GHG emission reduction potentials are addressed for several Western European countries together with a relative positioning of their electricity generation. GHG emission reduction potentials are also compared with simulation results. GHG emission reduction potentials tend to be significant. The Netherlands have a very widespread switching zone, so GHG emission reduction is practically independent of electricity generation. Other counties, like Germany, Spain and Italy could reduce GHG emissions significantly by switching. With an allowance cost following the switch level of a 50% efficient gas-fired plant and a 40% efficient coal-fired plant in the summer season (like in 2005), the global GHG emission reduction (in the electricity generating sector) for the eight modeled zones could amount to 19%.

In the present work, a novel parabolic trough solar collector model has been developed and validated. The validation has been carried out through a comparison with results of previous studies conducted in the worldwide most renowned laboratories, i.e., Sandia National Laboratory (SNL) and National Renewable Energy Laboratory (NERL). The comparison with experimental data collected by SNL, during LS-2 tests at AZTRAK platform, has shown good agreement, particularly for the case of receivers with Cermet coating. When compared with the Engineering Equation Solver (EES) code developed by NREL, the novel model offers improvements in the accuracy of thermal performance prediction. It has been found that the proposed model in the present study predicts more accurately the thermal efficiency than EES; with an average uncertainty of 0.64% compared to 1.11% for ESS, in the case of Cermet coating. Nevertheless, minor inaccuracy in the estimation of heat losses at higher operation temperatures has been found due to the error propagation in the model. The present work also includes a survey of the design and manufacturing processes of the parabolic trough collector that are of a particular interest for developing this promising technology.

In the present work, a novel parabolic trough solar collector model has been developed and validated. The validation has been carried out through a comparison with results of previous studies conducted in the worldwide most renowned laboratories, i.e., Sandia National Laboratory (SNL) and National Renewable Energy Laboratory (NERL). The comparison with experimental data collected by SNL, during LS-2 tests at AZTRAK platform, has shown good agreement, particularly for the case of receivers with Cermet coating. When compared with the Engineering Equation Solver (EES) code developed by NREL, the novel model offers improvements in the accuracy of thermal performance prediction. It has been found that the proposed model in the present study predicts more accurately the thermal efficiency than EES; with an average uncertainty of 0.64% compared to 1.11% for ESS, in the case of Cermet coating. Nevertheless, minor inaccuracy in the estimation of heat losses at higher operation temperatures has been found due to the error propagation in the model. The present work also includes a survey of the design and manufacturing processes of the parabolic trough collector that are of a particular interest for developing this promising technology.

Commercially available techniques for the removal of 90% of CO2 from power station flue gas were investigated by H&G for British Coal Corporation. This work was part of British Coal's studies of CO2 abatement technologies. The two cases examined were based on:- 1. A A gas containing a low concentration of CO2 produced by a 500 MWe conventional pulverised fuel (PF) power station. 2. B A gas containing a high concentration of CO2 produced by a novel power station design in which coal is burnt in a mixture of oxygen and recycled flue gas. A screening of commercially proven CO2 removal processes established that for Case A either aqueous monoethanolamine (MEA) or hot potassium carbonate (HPC) were suitable. For Case B either HPC or gas permeation membranes were identified as suitable. An analysis of operating costs including the impact on plant efficiency, but excluding the cost of oxygen in Case B , revealed that for Case A the use of MEA would be 30% cheaper than the use of HPC;whereas for Case B a 50% operating cost advantage was identified for the use of membranes compared with HPC. Thus MEA and membranes were selected for further evaluation for cases A and B respectively. Capital cost estimates for the CO2 removal process areas were subsequently derived for the preferred options for the two cases. For a UK greenfield site, the capital costs of the CO2 removal sections were estimated to be £98 million for Case A. For Case B an estimated £36 million was obtained for purification of CO2 with a gas permeation membrane. Some major technical constraints for the selected process routes were identified including the detrimental effect on operability of particulate carryover from the FGD and the high levels of SO2.

Commercially available techniques for the removal of 90% of CO2 from power station flue gas were investigated by H&G for British Coal Corporation. This work was part of British Coal's studies of CO2 abatement technologies. The two cases examined were based on:- 1. A A gas containing a low concentration of CO2 produced by a 500 MWe conventional pulverised fuel (PF) power station. 2. B A gas containing a high concentration of CO2 produced by a novel power station design in which coal is burnt in a mixture of oxygen and recycled flue gas. A screening of commercially proven CO2 removal processes established that for Case A either aqueous monoethanolamine (MEA) or hot potassium carbonate (HPC) were suitable. For Case B either HPC or gas permeation membranes were identified as suitable. An analysis of operating costs including the impact on plant efficiency, but excluding the cost of oxygen in Case B , revealed that for Case A the use of MEA would be 30% cheaper than the use of HPC;whereas for Case B a 50% operating cost advantage was identified for the use of membranes compared with HPC. Thus MEA and membranes were selected for further evaluation for cases A and B respectively. Capital cost estimates for the CO2 removal process areas were subsequently derived for the preferred options for the two cases. For a UK greenfield site, the capital costs of the CO2 removal sections were estimated to be £98 million for Case A. For Case B an estimated £36 million was obtained for purification of CO2 with a gas permeation membrane. Some major technical constraints for the selected process routes were identified including the detrimental effect on operability of particulate carryover from the FGD and the high levels of SO2.

Abstract In the current paper, an artificial neural network (ANN) model for predicting the performance of a liquid desiccant dehumidifier in terms of the water condensation rate and dehumidifier effectiveness is proposed. Six air and desiccant inlet parameters were used as inputs for the ANN. To determine the performance of the ANN technique for predicting the performance of the dehumidifier, actual experimental test data were obtained from a previous study, that tested a packed column dehumidifier with a total height of 0.6 m and a specific packing material surface area of 77 m2/m3, using triethylene glycol as the desiccant. In the experiment, 54 data samples were used in a series of runs. MATLAB code was designed to study feed forward back propagation with traingdm, learngdm, MSE, and tansig as the training, learning, performance, and transfer functions, respectively. Up to 70% of the experimental data was used to train the model; the remaining 30% was used to test the output. The results show that the 6-3-3-1 network structure was the best model for predicting water condensation rate, whereas the 6-6-6-1 network structure was the best model for predicting dehumidifier effectiveness. The maximum percentage difference between the ANN and experimental value for water condensation rate and dehumidifier effectiveness were 8.13% and 9.0485%, respectively. The model for the water condensation rate and dehumidifier effectiveness could be further improved by modifying the number of hidden layers.

Abstract In the current paper, an artificial neural network (ANN) model for predicting the performance of a liquid desiccant dehumidifier in terms of the water condensation rate and dehumidifier effectiveness is proposed. Six air and desiccant inlet parameters were used as inputs for the ANN. To determine the performance of the ANN technique for predicting the performance of the dehumidifier, actual experimental test data were obtained from a previous study, that tested a packed column dehumidifier with a total height of 0.6 m and a specific packing material surface area of 77 m2/m3, using triethylene glycol as the desiccant. In the experiment, 54 data samples were used in a series of runs. MATLAB code was designed to study feed forward back propagation with traingdm, learngdm, MSE, and tansig as the training, learning, performance, and transfer functions, respectively. Up to 70% of the experimental data was used to train the model; the remaining 30% was used to test the output. The results show that the 6-3-3-1 network structure was the best model for predicting water condensation rate, whereas the 6-6-6-1 network structure was the best model for predicting dehumidifier effectiveness. The maximum percentage difference between the ANN and experimental value for water condensation rate and dehumidifier effectiveness were 8.13% and 9.0485%, respectively. The model for the water condensation rate and dehumidifier effectiveness could be further improved by modifying the number of hidden layers.

Abstract In this paper, two robust decentralised proportional integral (PI) control designs are proposed for load frequency control (LFC) with communication delays. In both methodologies, the PI based LFC problem is reduced to a static output feedback (SOF) control synthesis for a multiple delay system. The first one is based on the optimal H ∞ control design using a linear matrix inequalities (LMI) technique. The second control design gives a suboptimal solution using a developed iterative linear matrix inequalities (ILMI) algorithm via the mixed H 2 / H ∞ control technique. The control strategies are suitable for LFC applications that usually employ PI control. The proposed control strategies are applied to a three control area power system with time delays and load disturbance to demonstrate their robustness.