A Hierarchical Architecture for the Coordination of an Ensemble of Steam Generators

This work presents a hierarchical architecture for the optimal management of an ensemble of steam generators, which needs to jointly sustain a common load. The coordination of independent subsystems is provided by a multi-layer control scheme. A high-level optimizer computes the optimal shares of production to be allocated to single generators. At medium level, a robust tube-based model predictive control (MPC) is proposed to track the time-varying demand of the ensemble using a centralized, but aggregated model, whose order does not scale with the number of subsystems. At low level, decentralized controllers are in place to stabilize the internal boiler pressure. The control architecture enables the dynamic modification of the ensemble configuration and plug and play operations. Simulation results are reported to demonstrate the potentialities of the proposed approach

Economic and environmental impacts of the energy source for the utility production system in the HDA process

The well-known benchmark process for hydrodealkylation of toluene (HDA) to produce benzene is revisited in a multi-objective approach for identifying environmentally friendly and cost-effective operation solutions. The paper begins with the presentation of the numerical tools used in this work, i.e., a multi-objective genetic algorithm and a Multiple Choice Decision Making procedure. Then, two studies related to the energy source involved in the utility production system (UPS), either fuel oil or natural gas, of the HDA process are carried out. In each case, a multi-objective optimization problem based on the minimization of the total annual cost of the process and of five environmental burdens, that are Global Warming Potential, Acidification Potential, Photochemical Ozone Creation Potential, Human Toxicity Potential and Eutrophication Potential, is solved and the best solution is identified by use of Multiple Choice Decision Making procedures. An assessment of the respective contribution of the HDA process and the UPS towards environmental impacts on the one hand, and of the environmental impacts generated by the main equipment items of the HDA process on the other hand is then performed to compare both solutions. This ‘‘gate-to-gate’’ environmental study is then enlarged by implementing a ‘‘cradle-togate’’ Life Cycle Assessment (LCA), for accounting of emission inventory and extraction. The use of a natural gas turbine, less economically efficient, turns out to be a more attractive alternative to meet the societal expectations concerning environment preservation and sustainable development

Fast model predictive control for hydrogen outflow regulation in ethanol steam reformers

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In the recent years, the presence of alternative power sources, such as solar panels, wind farms, hydropumps and hydrogen-based devices, has significantly increased. The reasons of this trend are clear: contributing to a reduction of gas emissions and dependency on fossil fuels. Hydrogen-based devices are of particular interest due to their significant efficiency and reliability. Reforming technologies are among the most economic and efficient ways of producing hydrogen. In this paper we consider the regulation of hydrogen outflow in an ethanol steam reformer (ESR). In particular, a fast model predictive control approach based on a finite step response model of the process is proposed. Simulations performed using a more realistic non-linear model show the effectiveness of the proposed approach in driving the ESR to different operating conditions while fulfilling input and output constraints.Peer ReviewedPostprint (author's final draft

The application of a new PID autotuning method for the steam/water loop in large scale ships

In large scale ships, the most used controllers for the steam/water loop are still the proportional-integral-derivative (PID) controllers. However, the tuning rules for the PID parameters are based on empirical knowledge and the performance for the loops is not satisfying. In order to improve the control performance of the steam/water loop, the application of a recently developed PID autotuning method is studied. Firstly, a 'forbidden region' on the Nyquist plane can be obtained based on user-defined performance requirements such as robustness or gain margin and phase margin. Secondly, the dynamic of the system can be obtained with a sine test around the operation point. Finally, the PID controller's parameters can be obtained by locating the frequency response of the controlled system at the edge of the 'forbidden region'. To verify the effectiveness of the new PID autotuning method, comparisons are presented with other PID autotuning methods, as well as the model predictive control. The results show the superiority of the new PID autotuning method

Modelling of a post-combustion CO₂ capture process using neural networks

This paper presents a study of modelling post-combustion CO₂ capture process using bootstrap aggregated neural networks. The neural network models predict CO₂ capture rate and CO₂ capture level using the following variables as model inputs: inlet flue gas flow rate, CO₂ concentration in inlet flue gas, pressure of flue gas, temperature of flue gas, lean solvent flow rate, MEA concentration and temperature of lean solvent. In order to enhance model accuracy and reliability, multiple feedforward neural network models are developed from bootstrap re-sampling replications of the original training data and are combined. Bootstrap aggregated model can offer more accurate predictions than a single neural network, as well as provide model prediction confidence bounds. Simulated CO₂ capture process operation data from gPROMS simulation are used to build and verify neural network models. Both neural network static and dynamic models are developed and they offer accurate predictions on unseen validation data. The developed neural network models can then be used in the optimisation of the CO₂ capture process

Operating Point Optimization of a Hydrogen Fueled Hybrid Solid Oxide Fuel Cell-Steam Turbine (SOFC-ST) Plant

This paper presents a hydrogen powered hybrid solid oxide fuel cell-steam turbine (SOFC-ST) system and studies its optimal operating conditions. This type of installation can be very appropriate to complement the intermittent generation of renewable energies, such as wind generation. A dynamic model of an alternative hybrid SOFC-ST configuration that is especially suited to work with hydrogen is developed. The proposed system recuperates the waste heat of the high temperature fuel cell, to feed a bottoming cycle (BC) based on a steam turbine (ST). In order to optimize the behavior and performance of the system, a two-level control structure is proposed. Two controllers have been implemented for the stack temperature and fuel utilization factor. An upper supervisor generates optimal set-points in order to reach a maximal hydrogen efficiency. The simulation results obtained show that the proposed system allows one to reach high efficiencies at rated power levels.This work has been carried out in the Intelligent Systems and Energy research group of the University of the Basque Country (UPV/EHU) and has been supported by the UFI11/28 research grant of the UPV/EHU and by the IT677-13 research grant of the Basque Government (Spain) and by DPI2012-37363-CO2-01 research grant of the Spanish Ministry of Economy and Competitiveness

Sensitivity analysis applied to the multi-objective optimization of a MCFC hybrid plant

In this paper, the multi-objective optimization of a molten carbonate fuel cell (MCFC) based hybrid plant fueled with landfill gas is performed. System operation is significantly affected by off-design conditions. These are due to variations methane concentration occurring as the landfill depletes, performance degradations of the components, particularly the fuel cell, and ambient conditions. For these reasons, the objective functions are defined considering the plant lifetime. Some of the parameters affecting the results, as the voltage degradation, the cost of fuel cell, the methane concentration and the unit cost of landfill gas can be only estimated or forecasted and their actual values are uncertain. Therefore, the optimization is performed considering a sensitivity analysis in order to estimate the effects of possible variations on the Pareto front. The following free design variables are considered: pressure and temperature operation of the MCFC, turbine inlet temperature, fuel mass flow rate. In addition, the optimal configuration of the heat exchanger network is selected for each set of the design variabl

Optimal Linear Parameter-Varying Control Design for a Pressurized Water Reactors

The applicability of employing parameter-dependent control to a nuclear pressurized water reactor is investigated. The synthesis techque produces a controller which achieves specified performance against the worst-case time variation of a measurable parameter which enters the plant in a linear fractional manner. The plant can thus have widely varying dynamics over the operating range. The results indicate this control technique is comparable to linear control when small operating ranges are considered