Abstract

Traditional building automation controllers are having low performance in dealing with non-linear phenomena. In recent years, model predictive control (MPC) has become a notable control algorithm for building automation system capable of handling non-linear processes. Performance of model-based controllers, such as MPC, is depending on reasonably accurate process models. For a building using baseboard radiator heater, a non-linear model is a more reliable representation of heat distribution system. Therefore, this study aims to present a non-linear gray-box model for a residential building connected to the local district heating network that is equipped with radiator heat emitters. The model is supposed to forecast the indoor air temperature as well as the radiator secondary return temperature. The model is validated using measurements collected from a building in Västerås, Sweden. In addition to a better accuracy, another motivation behind using a non-linear heating circuit model is to enhance its generalization performance. With the added benefits of accuracy and generalization, this model is expected to extend practical MPC implementation for such buildings.

Abstract

The Euro 3 Koreans heavy-duty diesel engines produce the high NOx pollutant and harmful for human body. The effective method to reduce nitrogen oxide (NOx) pollutions is the selective catalytic reduction (SCR) system. This study deals with problems in the urea evaporation and decomposition process in heavy duty diesel engine with 12.000cc. The ammonia quantity and quality were sampled at the catalyst inlet using a 18 gas sensor, and samples of NOx from each urea injector were compared by experiments and simulations using STAR-CCM+ software. The results elucidate the saturation phenomena, vaporization phenomena, urea distribution patterns, and NOx reduction efficiency value urea injectors.

Abstract

This paper presents a predictive probabilistic approach (PPA) for the optimal sizing of new distributed generation capacities in support of the main grid to respond to a fraction of the total load during the supply current interruption duration defined in using renewable-based microgrid assets. The model is used to simulate network failures by disabling the network for a certain time step. The load profile can be changed during network interruptions to represent a critical load. The generation capacity of the microgrid must be able to generate the value in grid-connected mode while supporting a critical load during a grid outage. The probability of loss of load characterizes the adequacy and energy not supplied represents the estimated reliability and the associated cost. The approach allows all technologies in the model to be evaluated, both during grid-connected mode and during grid outages when technologies can continue to power critical loads as part of the microgrid.

Abstract

North Rhine-Westphalia (NRW) is the industrial center of Germany and one of the most important industrial locations in Europe. It is a key location for the energy-intensive basic materials industry like the production of steel and non-ferrous metals, (petro)chemicals, cement and lime, bricks, glass and ceramics, and paper. Around 20 % of NRW’s total greenhouse emissions derive from industrial processes. By 2045, industry must achieve climate-neutrality, which requires a massive transformation effort. Technologically, this needs large-scale utilization of green hydrogen, carbon management, consequent circular economy, and climate-neutral production of process heat. Furthermore, various adjustments to the policy framework are essential.

Abstract

Electrification of steam methane reforming is an interesting approach towards the EU carbon neutrality target. This paper presents a dynamic continuous-stirred-tank-reactors (CSTRs) model for simulating an electrically heated washcoat tube that represents a steam methane reforming reactor. Results from the engineering model are presented, such as temperature, gas molar fraction, CH4 conversion, and effects of dynamic operation. The model is validated by a previously published CFD model, which has been validated by experimental data. The engineering model provides much faster results than the CFD model and can accurately re-produce the CH4 conversion and the temperature profile in the reactor.

Abstract

Nowadays, global carbon dioxide emissions into the atmosphere have reached a historically high level due to carbon dioxide emissions. The current effort to change the alternative energy source of LNG fuel was found to be one of the cleanest fossil fuels due to lower carbon emissions to ease the rapid growth of carbon emissions.
LNG marine industry forecasts that the increasing demand for FLNG vessels will accelerate the development of gas resources, research work on the system process analysis and optimisation of the Liquefaction system. The rapid growth in equipment and processes of FLNG development is the response to the challenges due to weight and space limits.
One of the critical objectives onboard FLNG is to build the combined plant models of clean energy. The research boundary covers processes of LNG liquefaction, nitrogen separation and boil-off gas handling. BOG re-liquefaction will be used as a fuel gas supply system. BOG will also be converted to clean energy fuel such as hydrogen or ammonia for the main and auxiliary engines onboard.
A small-scale liquefaction process with a refrigerant cycle is proposed in this study to meet these FLNG challenges. The Brayton refrigeration (BR) cycle is found to be most suitable for FLNG vessels, among other refrigerant cycles. The BR cycle using nitrogen as a refrigerant and a single expander is the focus of this study [8].

Abstract

Accurate capacity estimation is crucial to ensure operational safety of Li-ion battery. In this paper, a novel capacity estimation approach is proposed for Li-ion battery cell. Two voltage-related features on probability density function based incremental capacity curve and average temperature are extracted as healthy indicators. Regression between healthy indicators and capacity is constructed using random forests. Results show that the capacity estimation error could be controlled within 2.5% throughout the whole lifecycle of the battery.

Abstract

The objective of this work is to study heat and mass transfer processes in a single biomass particle before its thermal degradation (< 200 °C) under high intense acoustic fields. For that, was developed a numerical code for Biot number higher that one, i.e., non-isothermal particles. The hypothesis is that an acoustic field alters the interaction between the gas and particles, proving drying. Acoustic fields can be obtained by using a loudspeaker inside a reactor. The proposed model predicts moisture mass transfer completion for different particle sizes and oscillating frequencies. The obtained data are relevant for plant conversion capacity and reactor's preliminary design.

Abstract

The sizing of long duration storage is one of the main challenges in the study of the feasibility of low-carbon power systems. In reviewing the literature, we identify that the consideration of technical and environmental constraints as well as uncertainty in production and consumption impact its sizing. We then determine that, with a classical unit commitment optimization, the choice of the simulation horizon, as well as the length of that horizon and the combination of years on which the study is carried out have a significant impact on the sizing of long duration storage. The reasons for this are the different long duration storage (LDS) discharge need profiles in different years and the sizing method used. We also found that the sequence of meteorological events significantly impacts the LDS sizing. Hence, our need for LDS considerably increases compared to the results proposed by the literature which in most cases doesn’t consider those methodological aspects. This finding calls for the development of more robust methods for sizing long duration storage as well as further research on the LDS role in high penetration variable renewable energy (VRE) power systems.

Abstract

A methane (CH4) slip is normally un-avoided during biogas upgrading, and water scrubbing is the most widely adopted upgrading technology. As CH4 is also a key greenhouse gas, such a slip can damage the carbon neutrality of bioenergy and result in a positive emission. In order to eliminate the negative influence, a post treatment to handle the released CH4 is essential. Regenerative thermal oxidation (RTO) is a commercially available air pollution control technology, and it can be used for the post treatment. This paper aims to analyze the technical and environmental performance of RTO for removing CH4 from the waste gas of biogas upgrading by water scrubbing. A three-dimensional numerical model was developed for the thermal flow-reversal reactor (TFRR). CH4 content in waste gas is investigated as the key factor, and the energy consumption, the amount of CH4 elimination and associated CO2 equivalent avoidance are estimated as key performance. It was found that the higher CH4 content benefits maintaining the operation of RTO. With the increase of CH4 content, the energy consumption of CH4 removal decreases. For example, it decreases from 8.05kWh/kg to 1.22kWh/kg when CH4% rises from 0.28% to 0.42%. The case study on a real biogas plant that produces 3909ton biogas per year shows that removing CH4 corresponds to a CO2 equivalent avoidance of 231.38ton/year.