Modelling and Simulation of Dynamic Behaviour in Crushing Plants

Licentiate thesis, 2013

Crushing plants are a vital part in the production of aggregates and metals. Plants are traditionally simulated with a steady-state simulation. With steady-state simulation the plant is simulated until equilibrium is achieved. However, crushing is a continuous process and as such it is subjected to variations and changes in performance depending on the dynamics of the system. A different technique is therefore necessary to estimate the real behaviour of the plant. The main hypothesis in this research is that crushing plants are affected by both gradual and discrete changes in the process which alter the performance of the entire system, making it dynamic. A dynamic simulation is defined here as continuous simulations with sets of differential equations to reproduce the dynamic behaviour of a system. Crushing plants experience different operating performance depending on the configuration of each individual process unit, the configuration of the plant, the design of the control system, events occurring in the process and additional disturbances. Three different application areas for dynamic simulation have been demonstrated in this thesis: plant performance, process optimization and operator training. Each of these areas put different constraints on the modelling and simulation of crushing plants. Traditional steady-state plant simulations are able to provide an overall estimation of the ideal performance. Plants can however experience changes in performance during operation. Plant simulation for an unstable plant has been performed in order to increase the level of stability of the operation. Simulations and experiments of different operational strategies revealed that a higher level of stability was possible with different configuration on specific production units. Development of control systems is important for the operating conditions in a crushing plant. Real-time optimization is a relatively new field in aggregate production which aims to optimize the production in real-time with the help of advanced process control algorithms. One way of achieving this is with a Finite State Machine. By connecting a Finite State Machine algorithm to a dynamic simulator, tuning of the control parameters becomes possible. Even though only a marginal improvement was estimated with optimized parameters, located with a genetic algorithm, it is an important step in control system development. The system structure required to enable operator training was built in the Chalmers Rock Processing System laboratory. The fundamental framework around the system is built on the dynamic simulator. The process is simulated in real-time and information from the simulated process is communicated to a Human Machine Interface and a Programmable Logic Controller. With this system, different scenarios can be simulated to assist the operator in gaining knowledge and experience. In conclusion, dynamic simulation of production processes has the ability to provide the user with deeper understanding about the simulated process, details that are usually not available with traditional steady-state simulations. Multiple factors can affect the performance of a crushing plant, factors that need to be included in the simulation to be able to estimate the actual plant performance. The dynamic response of a system is determined by the characteristics of the system involved and the changes in the process.