Modelling of Coupled Mass and Thermal Balances in Hall-Heroult Cells during Anode Change

As the most routine work practice in an aluminum reduction cell, anode change introduces substantial perturbations that adversely affect the mass and heat balance of the cell which may lead to loss of process efficiency and increased energy consumption. The literature lacks a dynamic mathematical model that describes the interactions among cell variables (e.g., electrolyte temperature and flow, anode current distribution, and cell heat loss) during and following this operation, which could otherwise be used to understand the process in greater depth and to develop changes that improve process operations and control. This paper presents a spatially-discretised dynamic model for anode replacement that integrates mass balance, thermal balance and cell voltage to describe and predict local cell variables. It was experimentally validated with an industrial cell undergoing an anode change procedure. This generic model can be applied to different cell design or process conditions by using appropriate parameters (e.g., heat transfer coefficients, conductivities, and flow patterns). The model can be used to improve existing process operations or control strategies for higher process efficiency, lower energy consumption, and lower emissions

Electrodeposited lead dioxide coatings

Lead dioxide coatings on inert substrates such as titanium and carbon now offer new opportunities for a material known for 150 years. It is now recognised that electrodeposition allows the preparation of stable coatings with different phase structures and a wide range of surface morphologies. In addition, substantial modification to the physical properties and catalytic activities of the coatings are possible through doping and the fabrication of nanostructured deposits or composites. In addition to applications as a cheap anode material in electrochemical technology, lead dioxide coatings provide unique possibilities for probing the dependence of catalytic activity on layer composition and structure (critical review, 256 references)

Self-interest Distributed Economic Model Predictive Control for Renewable Energy Generation and Battery Energy Storage

The deployment of distributed generation based on renewable energy sources, e.g., solar photovoltaic and wind turbines, has become increasingly widespread to reduce the dependence on fossil fuels and associated greenhouse gas emissions. However, the inherently intermittent nature of renewable energy generation can cause significant problems for the electricity network, including poorer power quality and reliability, and power imbalance between supply and demand that could threaten network stability. The distributed generation (DG), distributed battery energy storage systems (BESS) and loads can be integrated and form a microgrid (MG). DG can lead to significant economic savings mainly due to reduction of network transmission losses as generation is connected closer to the loads. In this paper, we develop a scalable distributed energy storage and power management approach. In the proposed approach, each individual BESS in the MG is controlled by an autonomous battery controller (BC). The MG power price is determined by a Pricing Controller (PC) based on the collective power demand and supply and their future predictions. Each active customer sells or buys power from other active customers or the Main Network (MN) depending on the power generated by the local generation, current (actual) and predicted consumption (load), state of charge of local BESS, current and predicted future electricity price (the prices for electricity to be traded within the MG and from the MN) and weather forecast etc. Each BC also communicates with a PC to form a controller network. Since individual microgrid users are usually financially independent, individual BCs should charge and discharge the batteries (buy or sell energy) based on the power price to optimize their own economic benefits. However, such “self-interest” nature may cause excessive energy trading and oscillations in power flows within the microgrid and/or between the microgrid and the utility grid, due to the positive feedback in response to power price. This may lead to degraded power quality and destabilized microgrid bus voltage, defeating the purpose of distributed BES for residential microgrids. Therefore, the BCs should also ensure the magnitudes of the fluctuations of the power flows to be limited to an acceptable range. Recently, there are a number of distributed control approaches based on Game Theory reported in literature. However, these approaches either solve the Nash equilibrium points as a centralized game problem, which is not scalable due to combinatorial explosion of possible strategies, or require iterative optimizations which can be impractical, when the number of subsystems in the MG becomes large. In this work, we have developed a self-interest distributed economic model predictive control approach for the distributed energy storage and power management problem. Each model predictive controller optimizes an economic cost that directly related to the economy of BESS operation. The MG-wide stability requirement is represented as the frequency-weighted L2 gain bound from the disturbances to the total power demand/supply in the MG. which is in turn cast into a dissipativity condition in a Quadratic Difference Form (QdF-dissipativity). This dissipativity condition is then converted into the dissipativity trajectory condition that each economic model predictive controller must satisfy. The proposed approach is scalable as it does not require online iterative optimizations across the controller network