the goals to achieve. To understand how these techniques help, one must first understand how model predictive control works. MPC is a general class of algorithms for feedback and feedforward control based on the receding horizon philosophy. This means a sequence of future optimal control actions is chosen according to a prediction of the short to medium term evolution of the system. MPC utilises a model of the process to predict the effect in various process variables, due to actual as well as future changes, in the manipulated outputs and feedforward variables. The sequence of moves for the manipulated variables is optimised in a multivariable fashion (as seen in Figure 2). When measurements (or new information) become available, a new sequence to replace the previous one is determined. Each sequence is computed employing an optimisation procedure, achieving two objectives; to optimise performance and to protect the system from constraint violations. In addition to MPC, the latest technologies - ranging from data access, analytics and visualisation to advanced modeling algorithms - are leveraged in ABB Ability™ APC to help customers optimise operations and drive their mill to its desired targets. LIME KILN OPERATION To understand how APC can optimise pulp mills, we are focusing on how it positively affected the lime kiln operations at one mill in Europe. The work on the pulp mill in Europe centred on the lime kiln. The lime kiln The latest technologies are leveraged in ABB AbilityTM APC to help customers optimise operations and drive their mill to its desired targets. The benefits of APC solutions for pulp mills include the opportunity to stabilise, improve process operations, and minimise the variation of key variables while considering process constraints. These operational benefits translate into financial gains due to increased throughput and minimised energy and raw material use. ABB Ability™ Advanced Process Control for pulp mills provides monitoring, predictive analytics and robust closed loop process control to optimise mill operations. The solution stabilises the process, reduces chemical usage and coordinates the numerous loops to incur optimum on-specification product quality at minimum variance. This technology is of particular value in the pulp and paper industry, where many processes are notoriously difficult to measure, and provides a better WORLD PULP&PAPER 91 Figure 2. Model Predictive Control enables performance optimisation by predicting outputs and calculating optimal input sequences. opportunity to model things that cannot directly be measured. LEVERAGING MODEL PREDICTIVE CONTROL WITHIN APC Traditional APC solutions rely on model predictive control (MPC) and state estimation strategies that use either a linear or non-linear mathematical model of the process and robust optimisation algorithms to estimate unmeasured states and control process variables. MPC is often used for control and optimisation of industrial processes, and is extremely efficient given the highly interactive, non-linear, multivariable dynamics of these processes. The use of these techniques for pulp and paper mills includes the development of non- linear mathematical models describing the process, and the design of a suitable cost function, which considers