Temperature modeling and control of the sulfuric acid plant in an industrial smelter
Date
2019-06-25
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Abstract
Roasting, smelting and converting are pyrometallurgical techniques used to eliminate gangue
rock from sulfide ores and produce a saleable metal product. Due to the large amount of sulfur
present in sulfide ores, the off-gas produced from pyrometallurgical processing is laden with
sulfur dioxide (SO2). Production of sulfuric acid (H2SO4), from off-gas laden with SO2 is one
of the methods used at smelters to reduce the amount of SO2 being released into the
atmosphere. The acid plant consists mainly of a catalytic converter, absorption towers and a
network of heat exchangers. SO2 is converted to sulfur trioxide (SO3) which is absorbed to
produce a sulfuric acid product. Efficient oxidation of SO2 occurs within a tight temperature
range so heat exchangers equipped with bypass valves are used to regulate the temperature
throughout the acid plant. In this dissertation, a steady-state model is developed from fundamental steady-state mass and
energy balances. The steady-state model provides a relation between the process variables and
the temperature of the outlet streams of the heat exchangers. Unknown variables are estimated
using industrial operating data. The steady-state model is used to investigate the effect of
process variables on the controlled variables. The disturbance variables that have the largest
effect on the process are the feed gas flow rate and the SO2 concentration. The results provide
useful information since with recent process modifications that are part of the Clean AER
project, variations in the feed gas flow rate and SO2 concentration will increase. The effects of
the manipulated variables were also investigated which provides a foundation of understanding for process control. The results of the investigation of the effect of process variables on the
controlled variables were quantified by calculating the steady-state gains.
The dynamics of the process were investigated through analyzing the industrial operating data.
The output variables do not vary simultaneously with changes in the input variables. The
correlation coefficients were determined for the variables. The correlation coefficient between
variables provides an estimate of how much influence the input variables have on the output
variables. Delayed correlation analysis was performed to explore the process dynamics.
Dynamic models were identified using industrial operating data with and without prior
information supplied using the System Identification toolbox in Matlab. Providing the steadystate gains and an estimate of the process time constants to the System Identification toolbox
greatly improved the identified model. The dynamic model was validated by comparing the
model-estimated output and the output from industrial operating data. Temperature control within the acid plant is a multiple-input-multiple-output control problem.
Bristol’s Relative Gain Array and Singular Value Analysis are used to determine the most
effective pairing of variables. A feedforward-feedback control scheme for temperature
regulation is explored. Simulations for major disturbances, such as flow rate and SO2
concentration of the feed gas, are carried out using two alternative controller pairings. The
results of the simulations are reviewed and the advantages of each controller pairing are
discussed.
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Keywords
steady-state model, process variables, controlled variables, feed gas flow rate, SO2 concentration, Clean AER project, temperature control, acid plant