On the realization of linear multivariable control systems

http://archive.org/details/onrealizationofl00tha

Rotary cylinders : solid transport prediction by dimensional and rheological analysis

A semi-experimental model for predicting the axial transport of the granular bed in a rotating cylinder is proposed. It is based on dimensional analysis and on the determination of an apparent viscosity characterizing the flow behaviour of the bed. Unknown constants in the model are determined either by analysis or by tuning with experimental data. An example of such tuning is shown to work well. The model is capable of giving as a function of filling angle and friction angle, the axial velocity that varies along the cylinder axis. This is important for the control of the process taking place inside the cylinder

Performance analysis of the aluminum casting furnace

The casting furnace plays a central role in the production of aluminum. Its design and operation are complex and involve some 450 parameters. There is a need for a model to predict and analyze its performance. We propose a simplified model in which each main component of the furnace is treated as a 1-D heat conduction medium. Based on the equations of conservation of mass, energy, and chemical species, complemented by the equations of conduction and the Hottel's formulation of radiative heat transfer, this dynamic model can simulate any sequence of operations such as loading, heating, stirring, skimming ... that constitutes a batch, and can take into account other operational details such as the opening of doors. It is validated on a real furnace, then used to predict furnace performance in other modes of operation, and also to determine an optimal fuel flow that minimizes a chosen cost function

Neural Generalized Predictive Controller for Induction Motor

In this paper the authors present a new advanced control algorithm for speed and flux tracking of an induction motor. This algorithm called: Neural Networks Generalized Predictive Control (NNGPC) uses a combination of Artificial Neural Networks (ANN) and Generalized Predictive Control technique (GPC). This later is traditionally used for systems characterised by a slow dynamic as in chemical process control. The NNGPC algorithm is based on the use of ANN as a nonlinear prediction model of the motor. This modeling technique is done by using the data from the system inputs/outputs information without requiring the knowledge about machine parameters. The outputs of the neural predictor are the future values of the controlled variables needed by the optimization procedure, which is achieved by minimizing a cost function with the reference control model using the Newton-Raphson optimization algorithm. The reference control model is carried out from an open loop control strategy of the induction motor. Simulation results show the effectiveness of the proposed control method

Feedback control systems : design with regard to sensitivity

http://archive.org/details/feedbackcontrols00rungN

Numerical simulation of a casting furnace

Industrial furnaces constitute a complex, nonlinear, distributed-parameter thermal system. The usual way to model them is to write the equations describing the physical phenomena. The analytic model thus obtained is often too complex to be useful for control and optimization purposes. We propose to build a linear model by running the analytic model to obtain the simulated data, then apply the least square approximation to those data to obtain the linearizing coefficients. This statistical approach is simple to implement and yields linear models with good representativity. The annoying aspect is that some of the coefficients thus obtained may not lend themselves to a direct interpretation of the physical process. But with proper consideration, this is no obstacle to the use of the model

Model-based optimization of the operation of the coke calcining kiln

A mathematical model was recently developed to simulate the calcination process of regular petroleum coke suitable for aluminum industry applications. The model is made of 14 ordinary differential equations describing energy and mass conservation in the gas and in the coke bed, and complemented by correlations and algebraic equations. It calculates temperature and concentration profiles in the kiln, and also yields other information important to kiln operation, such as calcined coke recovery factor and coke loss through the generation of dust. In this paper it is demonstrated that the model is an efficient tool for the optimization of kiln operation. The model is used to study the effect of key control variables upon kiln operation and productivity. Further, it is shown that higher kiln productivity may be obtained with optimized kiln control and without loss of satisfactory kiln operating condition

Trends in intelligent process control methods in the primary aluminum industry

This progress report presents new trends and work underway in research related to process control methods for the primary aluminum industry, based on process modeling combined with advanced control techniques using computational intelligence. Promising applications are seen for a wide range of process control situations from calcining kilns to electrolytic cells and casting furnaces

Model-based control for industrial processes using a virtual laboratory

In the metallurgical industries, thermophysical processes are used in large numbers for the processing of materials in successive stages. Those processes are complex and they operate in hostile conditions and with poor accessibility. Model-based control in such cases is useful for designing and testing control strategies. The concept of virtual laboratory consists in combining real and virtual processes with real and virtual controllers, these four elements communicate with one another locally or through the internet. Researchers in the laboratory and operators on the plant floor can work together at a distance and in real time to solve process control problems by applying various control strategies and testing the solutions. This paper reports the work undertaken for setting up the virtual control laboratory (VCL) and gives an example of model-based control design carried out as an application of the VCL concept. The example is drawn from the adaptive control design of an aluminium casting furnace

Modelling of the coke calcining kiln

Modelling the rotary kilns used for the calcination of the coke is no simple undertaking, due to the manyinteracting physical phenomena governing the behavior of the solid bed and the freeboard gas. To date, a few attempts havebeen made, resulting in models that are either limited to a static global heat balance evaluation or affected by weaknessescaused by too many crude simplifying assumptions. As the main purpose is to control coke quality by controlling the calcination temperature and its rate ofincrease, the key issue is to control the temperature and location of the calcining zone, and in order to do so, it isimportant to introduce the effect of the many critical factors such as emissivities, volatiles and third air into the model. We have succeeded in doing so. Simulation results will be presented and discussed, clearly showing a possibility of optimizingthe operation of the kiln