Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot

This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell

Holistic modelling techniques for the operational optimisation of multi-vector energy systems

Modern district energy systems are highly complex with several controllable and uncontrollable variables. To effectively manage a multi-vector district requires a holistic perspective in terms of both modelling and optimisation. Current district optimisation strategies found in the literature often consider very simple models for energy generation and conversion technologies. To improve upon the state of the art, more realistic and accurate models must be produced whilst remaining computationally and mathematically simple enough to complete within short periods. Therefore, this paper provides a comprehensive review of modelling techniques for common district energy conversion technologies including Power-to-Gas. In addition, dynamic building modelling techniques are reviewed as buildings must be considered active and flexible participants in a district energy system. In both cases, a specific focus is placed on artificial intelligence-based models suitable for implementation in the real-time operational optimisation of multi-vector systems. Future research directions identified from this review include the need to integrate simplified models of energy conversion units, energy distribution networks, dynamic building models and energy storage into a holistic district optimisation. Finally, a future district energy management solution is proposed. It leverages semantic modelling to allow interoperability of heterogeneous data sources to provide added value inferencing from contextually enriched informatio

Biomass for Energy Country Specific Show Case Studies

In many domestic and industrial processes, vast percentages of primary energy are produced by the combustion of fossil fuels. Apart from diminishing the source of fossil fuels and the increasing risk of higher costs and energy security, the impact on the environment is worsening continually. Renewables are becoming very popular, but are, at present, more expensive than fossil fuels, especially photovoltaics and hydropower. Biomass is one of the most established and common sources of fuel known to mankind, and has been in continuous use for domestic heating and cooking over the years, especially in poorer communities. The use of biomass to produce electricity is interesting and is gaining ground. There are several ways to produce electricity from biomass. Steam and gas turbine technology is well established but requires temperatures in excess of 250 °C to work effectively. The organic Rankine cycle (ORC), where low-boiling-point organic solutions can be used to tailor the appropriate solution, is particularly successful for relatively low temperature heat sources, such as waste heat from coal, gas and biomass burners. Other relatively recent technologies have become more visible, such as the Stirling engine and thermo-electric generators are particularly useful for small power production. However, the uptake of renewables in general, and biomass in particular, is still considered somewhat risky due to the lack of best practice examples to demonstrate how efficient the technology is today. Hence, the call for this Special Issue, focusing on country files, so that different nations’ experiences can be shared and best practices can be published, is warranted. This is realistic, as it seems that some nations have different attitudes to biomass, perhaps due to resource availability, or the technology needed to utilize biomass. Therefore, I suggest that we go forward with this theme, and encourage scientists and engineers who are researching in this field to present case studies related to different countries. I certainly have one case study for the UK to present

Advanced control algorithm for FADEC systems in the next generation of turbofan engines to minimize emission levels

New propulsion systems in aircrafts must meet strict regulations and emission limitations. The Flightpath 2050 goals set by the Advisory Council for Aviation Research and Innovation in Europe (ACARE) include reductions of 75%, 90%, and 65% in CO2, NOx, and noise, respectively. These goals are not fully satisfied by marginal improvements in gas turbine technology or aircraft design. A novel control design procedure for the next generation of turbofan engines is proposed in this paper to improve Full Authority Digital Engine Control (FADEC) systems and reduce the emission levels to meet the Flightpath 2050 regulations. Hence, an Adaptive Network–based Fuzzy Inference System (ANFIS), nonlinear autoregressive network with exogenous inputs (NARX) techniques, and the block-structure Hammerstein–Wiener approach are used to develop a model for a turbofan engine. The Min–Max control structure is chosen as the most widely used practical control algorithm for gas turbine aero engines. The objective function is considered to minimize the emission level for the engine in a pre-defined maneuver while keeping the engine performance in different aspects. The Genetic Algorithm (GA) is applied to find the optimized control structure. The results confirm the effectiveness of the proposed approach in emission reduction for the next generation of turbofan engines