As time progresses, aging of the anode baking furnaces occurs, and packing coke (the powder) gets infiltrated into the flue-wall cavity, which gives rise to flow blockage. Using a fully coupled 3D multi-physics computational model, several modified flue-wall designs are proposed. In the current design of the flue-wall, the openings at the top of first and last baffle are meant for the flow bypass in case of flow blockage. However, these openings are considered to be accountable for nonuniform distribution of the flow and extra energy consumption. The effect of flow blockage for different designs is investigated. By closing the openings which, both the flow and temperature uniformity is enhanced substantially. It is also remarked that the modified fluewall designs are safe to operate at the time of flow blockage. Moreover, considering the practicalities of modifying the flue-wall design, openings of different thickness are proposed by specifying the number of bricks to be removed. This modification is done by removing one brick (85 mm) as a reference case, and other scenarios are also considered by shifting the two openings to the top or the bottom. The methodology and results presented in the present research can be employed effectively by the aluminum industry in modifying the furnace geometrical and operational parameters to enhance baking uniformity for old furnaces.
2GJ of energy is required to bake one ton of carbon anodes which are heat-treated in the anode baking furnaces. Computational fluid dynamic (CFD) modeling is useful in conducting coupled transient heat transfer, turbulent fluid flow, and combustion simulations. However, due to the huge temporal and spatial domains, it is difficult and expensive computationally to use CFD models for the evaluation of the overall furnace operation. As a part of quality control in most of the modern aluminum smelters, large flue-gas and anode temperature measurements are available. The present study applies an artificial neural network-based method to better exploit these large datasets and gain new physical insight in a cost-effective manner. A shallow neural network is considered consisting of an input layer, a hidden layer, and an output layer. The data is divided into a training set (70%) and a validation set (30%), to avoid overfitting of the data. It is remarked that there is a good agreement between the fitted data and targeted values for both the validation set as well as the training set. For the case study, 147 epochs are required to reach the best validation performance, which is 84.9. The error between predicted and target values are mostly between 20C, which indicates a high accuracy in the prediction level. The neural network-based model can be effectively integrated into the anode baking process models to estimate anode baking uniformity more precisely. The methodology presented in the current research can be extended to simulate the entire anode baking process, preheating, firing and cooling sections, cost-effectively.
The present work aims to develop a learning-based approach for a demand-driven control system which can automatically adjust the HVAC set points and supply conditions in terms of the actual requirements of the conditioned space. Internal heat gains from typical office equipment, such as computers, printers and kettle will be the focus of this paper. Due to its irregular use during scheduled heating or cooling service periods, an opportunity is offered to reduce unnecessary energy demands of HVAC systems related to the actual use of the equipment and its heat gains, i.e. over- and underutilization of equipment indicate whether indoor spaces are required to be conditioned or not. The work will be using deep learning enable cameras which can locally run trained algorithms to analyze and take action based on how equipment is utilised in a space real time. This proposed strategy automatically responds to the equipment usage for optimizing energy consumption and indoor conditions. The work will compare the performance of the developed approach with a conventional approach such as the use of static heating or cooling profiles. To highlight its capabilities, building energy simulation was used and initial results showed that while maintaining thermal comfort levels, up to 11% reduction of the energy consumption can be achieved by the proposed strategy in the comparison to conventionally-scheduled HVAC systems, while only focusing on three types of equipment.
Higher efficiencies and more compact designs in spite of larger built-in volume ratios are associated with variable wall thickness scroll expander geometries. In this research paper, transient 3D CFD simulations of this scroll-type design were presented to examine the influence of radial clearances on the aerodynamic performance in small scale Organic Rankine cycle systems. The decrease of radial clearances resulted in high speed flank leakages. There was a sharp increase in the Mach number in conjunction with a decrease in the static pressure. Supersonic flows were generated through those gaps between the fixed and orbiting scroll. Thus, the radial clearances need to be sealed.
This paper focuses on the coordinated operation (CO) of a gas network and electricity network and proposed a novel framework for coordinated operation between them. In this paper, credit ranking (CR) indicator for coupling units was established, and gas consumption constraints information of natural gas-fired units (NGFUs) is generated, natural gas network operator (GNO) will deliver this information to electricity network operator (ENO). The greatest advantage of this operation framework is that no frequent information interaction between GNO and ENO is needed. The entire framework contains two participants and three optimization problems, which are GNO optimization sub-problem-A, GNO optimization sub-problem-B and ENO optimization sub-problem. Decision sequence changed from traditional ENO-GNO-ENO to GNO-ENO-GNO. For ENO optimization sub-problem, a Second-order Cone (SOC) relaxation was utilized and reformulated the original problem as Mixed-Integer Second-Order Cone Programming (MISOCP) problem. For GNO optimization sub-problem, an improve Sequential Cone Programming (SCP) method is applied based on SOC relaxation and converts the original sub-problem to MISOCP problem. Simulations demonstrate the effectiveness of the proposed framework.
In this paper, we propose a distributed optimisation approach based on Alternating Direction Method of Multipliers to optimise building energy consumption, reduce energy bills, and adopt demand response (DR) schemes at the building level by exploiting building flexibility. Different optimisation models are proposed to represent the types of flexibility offered by building devices and to incorporate DR incentives. The evaluations of the approach show significant energy cost saving and prove the feasibility of DR adoption.
The coal char reactivity is usually evaluated in TG at a low heating rate under ex-situ conditions, which is far from the real condition in boiler and gasifier. In this work, the isothermal CO2 gasification reactivity of in-situ chars which were pyrolyzed under different heating rates and terminal temperatures was measured by a rapid TG apparatus. Furthermore, the char structural parameters were correlated with the reactivity index. Results showed that heating rate had little effect on the gasification reactivity of the in-situ char when it exceeded 50oC /min. The carbon crystalline structure related closely to the reactivity index. Simultaneously, the HTSM experiments demonstrated that the reaction rates were slightly higher compared with TG.
The modern steel industry depends largely on blast furnace route to produce hot metal. Massive amount of CO2 is discharged in the blast furnace process through utilising carbon-based reducing agents e.g. coke to extract iron from iron ore. It is extensively acknowledged that renewable and biomass-generated reducing agents are gathering momentum to replace part of coke in ironmaking applications. Syngas produced from biomass gasification is mainly composed of hydrogen and carbon monoxide. In this paper, the model of blast furnace operation with biomass syngas injection is established by using Aspen Plus software. The simulation results demonstrate that the minimal coke consumption with syngas injection could be 320 kg·tHM-1 when an injection rate is about 60 kg·tHM-1 , while the coke consumption could be reduced to 316.5 kg·tHM-1 when 50 kg·tHM-1 of hydrogen is injected. With the optimal syngas injection rate, CO2 emissions of the blast furnace can be reduced by 40.8% when compared with that of typical operation when coke rate is 385 kg·tHM-1 .
The promotion of distributed renewable power generation and the development of power electronic converters have promoted the application of DC microgrid in households. In this paper, based on the existing household power structure, a home-based power router is proposed, and according to various operating scenarios in real life, a master-slave control strategy for converting the main control power supply is proposed for different operating conditions in gridconnected mode and island mode. By building the circuit model of household power router, monitoring the voltage fluctuation of DC bus and the power fluctuation of photovoltaic, energy storage, power grid and load, the practical control strategy of microgrid router is verified and analyzed.
Current renewable energy mounting technologies with its different installation methods, and mounting locations are consequently affected by wind loads differently. Using the FSI approach, this study evaluated solar panels attached to the gabled roof of a single detached low-rise building. The building was subjected to typhoon strength winds in an urban environment using Computational Fluid Dynamics (CFD) analysis. A typhoon’s Atmospheric Boundary Layer (ABL) flow simulation was conducted to predict the pressure coefficient distribution around the structure. A validated structural model of the support attached to the roof was then developed, and the analysis performed using FSI to predict deflections, stress concentration areas and potential failure in the structural supports and attachments. The results of the study showed the weaknesses in the current design considering the roof shape, pitch, structural support, arrangements and materials. Results show areas of failure in the panels with regards to wind angle direction and installation location.