HEAT TREATMENT OF METALS: PRINCIPLES, TECHNOLOGIES AND INDUSTRIAL PRACTICE

An automatic Steel Strip Annealing Furnace Control has been performed in order to minimize the energy consumption during both transition and steady state load conditions. The aim of the job is to realize a dynamic control of the strip thermal profile, whereas normally, the control is focused on the furnace temperature, because the direct strip temperature measures are partially available only. For this reason has been developed a dynamic linear State-Space (SS) model returning the continuous linear estimation of the strip temperatures. An Extended Kalman Filter (EKF) has been added to this model in order to provide the dynamic correction of the calculated temperature by means of the field available measures. The synthesis of a Linear Quadratic Gaussian (LQG) Regulator, completes the software, in order to perform in real time, the optimal control of the process. The main advantages observed applying this optimal control are the reduction of the transient time response of the process during load or sets variations, the minimization of energy losses during sets changes and the possibility to drive the whole thermal profile of the strip inside the furnace.

Several control strategies for reducing the energy consumption of continuous reheating furnaces are reviewed. The energy flows and the efficiencies occurring in such furnaces are analyzed. Moreover, the nexus between energy savings and emission reduction is discussed. A case study of an industrial slab reheating furnace shows how the implementation of a nonlinear model predictive controller for the slab temperatures has reduced the primary energy consumption by 9.6%.

In this study, heat treatment response of powder metallurgical Al-SiC composites were investigated. The samples were compacted by hot pressing technique using different parameters. Sintered compacts were solution treated at 540 °C for 8 hours and artificially aged at 200 °C for 2, 4, 6 and 8 hours. Microstructural and mechanical properties of the heat treated compacts were studied and compared to as cast samples in detail.

Over 400,000 metric tons of sulphuric acid are produced in an acid plant processing gases reach in SO2. The gas flow rate and the gas SO2 concentration depend on the operation of a copper smelter. The smelter is formed by one flash furnace and four Pierce Smith converters and two slag furnaces. Since the operation of the flash furnace is continuous meanwhile the operation of each converter is semi-batch and with different stages, the amount of gases produced and the SO2 concentration are time variant. Moreover, these differences produce important changes of pressure inside the gas ducts and in the gas collecting zones. If the vacuum is increased then the air aspirated into the system dilutes the gas. If the pressure in the gas collecting zones increases then a leak of gases to the ambient is produced. The last occurrence should be minimized in order to avoid environmental incidents. Finally, the acid plant performance is optimized when the flow rate and the SO2 concentration are kept inside narrow bands.A study on how the main variables were affecting the gas transport system, such as the flash furnace pressure, the mixing chamber pressure, the gas flow rate and SO2 concentration produced in each zone, is presented. In this study, to achieve a quasi-steady state operation in the acid plant, along with avoiding any environmental issues, the regulation of damper openings in ducts, and the regulation of blowers operating conditions were used.The improvement in the gas transport system operation was achieved through the development and implementation of a hybrid control system for the blowers. The hybrid control system integrates conventional (PID) control with artificial intelligence techniques (expert systems) to modify the dumpers openings in the plant, according to the different scenarios that may occur.The main results were a decrease of 70% in the variability of the pressure in the mixing chamber, resulting in an increment of 2% in the average and a decrement of 80% in the variability of the flow rate of gases towards the acid plant. Positive pressures in the mixing chamber were decreased from 7.8% of the time in manual operation to 0.3% of the time in automatic operation.

The manual scheduling of operations in a Converter Aisle for Nickel Smelting is both a time-consuming and tedious task. The challenge of manually scheduling this process involves several tasks on multiple units (such as tapping of matte from furnaces, charging of converters, blowing, skimming of slag from converters). Furthermore, these tasks need to be carried out subject to a number of operational constraints that include upper and lower matte level limits in the furnaces, and the number and timing of the blowing operations required for a converter batch. This already complex set of tasks is further complicated by emission limits which are dependent on weather conditions and are set with limited forewarning, which places additional constraints on the Converter Aisle operation. However, when process disruptions invalidate the nominal schedule, it falls on operators to rely on operating protocols and experience to navigate the process operations and obey operating constraints as best as possible. These considerations motivate the development and use of an optimal scheduling decision support system that is capable of timely process scheduling, while respecting operational constraints, to achieve a given objective. The work presented formulates the operation scheduling as a mathematical optimization problem, where the relationships between the material flows, compositions, operating procedures, event timing and various constraints are captured, and optimization objectives quantified. The problem is expressed in a standard mathematical form that is amenable to solution using commercial optimization software. The project explores optimization opportunities in order to identify optimal scheduling configurations that are not realizable based on human intuition due to the complexity inherent in the process.