Aluminum Smelting Process Research Articles

Applying fundamental data to reduce the carbon dioxide footprint of aluminum smelters

The aluminum smelting process is a strong emitter of CO2 with three major contributions: that arising from electrical energy generation and its utilization, the process conversion contribution linked with anode consumption and anode production, and the greenhouse gas equivalents of the intermittent perfluorocarbon (PFC) emissions. Fundamental studies of alumina solubility, the electrochemical mechanism for triggering the onset of PFC emissions, and the importance of both mixing and current density on the speed of termination of anode effects, help define better paths for process operation. In conjunction with advising prebake aluminum smelters on process optimization, the authors have successfully tested the differences in theory and practice, and applied fundamentals in the operating environment to change some of the installed control strategies, termination mechanisms, and work practices. These changes have improved performance and reduced the CO2 footprint. The overall process reductions achieved exceed 2.24 million tonnes of CO2 equivalents per year in smelters producing less than 3 million tonnes of aluminum per year.

Continuous Measurement of Peak Hydrogen Fluoride Exposures in Aluminum Smelter Potrooms: Instrument Development and In-Plant Evaluation

The aluminum smelting process continuously evolves both sulfur dioxide (SO 2 ) and hydrogen fluoride (HF) gases. The vast majority of these evolved gases are captured by local exhaust ventilation systems and transported to fume treatment centers. Any gas escaping the ventilation systems could create the potential for workplace exposures. Currently, there are no commercially available sensors that are capable of selectively measuring peak concentrations (< 10 sec) of HF in the presence of SO 2 . This measurement capability is critical for facilitating a better understanding of the etiology of respiratory health effects. This article presents the development and in-plant testing of a portable, tunable diode-based HF sensor that shows equivalent or improved performance relative to NIOSH Method 7902 and is capable of measuring short-term personal peak HF exposure potentials in operating aluminum smelters.

A calcite permeable reactive barrier for the remediation of Fluoride from spent potliner (SPL) contaminated groundwater

The use of calcite (CaCO 3) as a substrate for a permeable reactive barrier (PRB) for removing fluoride from contaminated groundwater is proposed and is illustrated by application to groundwater contaminated by spent potliner leachate (SPL), a waste derived from the aluminium smelting process. The paper focuses on two issues in the implementation of calcite permeable reactive barriers for remediating fluoride contaminated water: the impact of the groundwater chemical matrix and CO 2 addition on fluoride removal. Column tests comparing pure NaF solutions, synthetic SPL solutions, and actual SPL leachate indicate that the complex chemical matrix of the SPL leachate can impact fluoride removal significantly. For SPL contaminant mixtures, fluoride removal is initially less than expected from idealized, pure, solutions. However, with time, the effect of other contaminants on fluoride removal diminishes. Column tests also show that pH control is important for optimizing fluoride removal with the mass removed increasing with decreasing pH. Barrier pH can be regulated by CO 2 addition with the point of injection being critical for optimising the remediation performance. Experimental and model results show that approximately 99% of 2300 mg/L fluoride can be removed when CO 2 is injected directly into the barrier. This can be compared to approximately 30–50% removal when the influent solution is equilibrated with atmospheric CO 2 before contact with calcite.

Typical architectures for fuzzy control

Some typical structural schemes of Fuzzy control have been surveyed. Besides general structure of fuzzy logic controller (FLC), the structural schemes include PID fuzzy controller, self-organizing fuzzy controller, selftuning fuzzy controller, self-learning fuzzy controller, and expect fuzzy controller, etc. This survey focuses on the control principle, and provides a basis for potential applications. Most of the structures have been used in various control fields, one of application areas is in the metallurgy industry, e.g., the temperature control of the electric furnace, the control of the aluminum smelting process, etc. According to the application requirements, one can choose a structural scheme for special use.

Scheduling Production in a Heavily Constrained Plant – Anode Manufacture in an Aluminium Smelter

In this paper a mathematical model of an anode manufacturing plant is developed with the objective of assisting in the planning and scheduling of production for up to a month ahead. A model of the overall smelter already exists and is based on a monthly time frame. The anode plant is a part of this model but is a very heavily constrained area and it is necessary to have a daily planning model that will help to achieve the optimal level of operations as dictated by the overall model of the smelter. The anode area is responsible for the production of carbon blocks (called anodes) which are an integral part of the aluminium smelting process. The plant is also responsible for the delivery of all raw materials within the smelter. The model developed is a daily one replicated for up to a month ahead, interconnected by opening and closing stocks.

Aluminum Production Modeling—A Nonlinear Bilevel Programming Approach

This paper develops a nonlinear bilevel programming model of an aluminium smelter that is capable of representing all the major production processes. The model encompasses all the areas of the smelter which operates in a multilevel way. However, as shown, it can be reduced quite simply to a bilevel programming problem. The problem specification involves nonlinearities with respect to the variables and the presence of ratios among the constraints. The problem is also characterized by a two-way material flow in the production process. A relatively simple solution algorithm that facilitates the attainment of the global optimum is developed. While the problem specification appears daunting, a number of simplifications can be made, due to nature of the problem, allowing its quick solution. While the model developed in this paper together with the solution algorithm appear simple, considerable effort was required to identify the fundamental relationships in the aluminium smelting process, quantify them, and then develop appropriate expressions to represent them. It is believed that the solution of this class of nonlinear bilevel programming problems will have implications for other applications.

Belief Transforms and the Comparison of Hypotheses

We describe a general approach to the comparison of two stochastic specifications over a collection of random quantities and then extend the comparison to collections of stochastic specifications. This comparison derives from the eigenstructure of the belief transform, which we construct in full generality for partially specified belief structures. We describe an application of the methodology, namely the comparison of hypotheses. Given various competing probabilistic specifications for a collection of observable quantities, we use the belief transform to separate those quantities for which the different models make very different predictions (and therefore which may be used to distinguish between the models) from those quantities for which the different models make similar predictions (and therefore which may be used to assess the suitability of the general class of models under consideration). Finally, we describe a particular application of hypothesis comparison which relates to the modelling of a collection of time series derived from an aluminum smelting process.

Technological developments for aluminum smeltinq as the industry enters the 21st century

The capital-intensive nature of aluminum smelting, with its low productivity per unit reactor and high electrical consumption rates, has motivated the search for alternative smelting processes to replace the aging Hall-Heroult technology. Optional routes include carbothermic reduction of alumina, chlorination followed by electrolysis of aluminum chloride, and electrolytic decomposition of alumina using inert electrodes. Still in need of some fundamental innovation, the alternative techniques are limited by unsatisfactory materials performance and reactor design constraints. There have been, however, significant advances in the process efficiencies and scale of both the Bayer process and the Hall-Heroult cells. As a result, the basic Hall-Heroult technology will continue as the dominant aluminum smelting process for at least the next 50 years.