AbstractsEngineering

Optimization of large-scale industrial systems requires not only state-of-the-art numerical algorithms, but also accurate tailor-made underlying models to ensure the solution is both sensible and useful. The combination of setting up a rigorous optimization solver together with building a high-fidelity model can cause the typical industrial user to become overwhelmed with formulating one or both of these steps, resulting in poor performance and/or a suboptimal solution. This work addresses the problem by developing a high-level framework for modelling and solving industrially significant optimization problems. The framework allows the user to concentrate on their domain specialization, while the framework automatically tailors the optimization problem by exploiting structural features within the user's model. To illustrate the benefit of this approach, two widely varying industrial optimization problems are investigated: Online optimization within an embedded predictive controller and large-scale steam utility system operational optimization. Within the first chosen example, an embedded model predictive controller, an optimal control problem must be solved at each sample in order to calculate the next control move(s). In a traditional linear predictive controller, this requires solving online a quadratic programming problem which, even for modest problems with relatively short prediction horizons, can involve tens of decision variables and hundreds of linear constraints. On an embedded platform, such as a microcontroller, solving a problem of this size online requires substantial computational power together with a large amount of dynamic memory, both of which are highly constrained on typical hardware. To overcome the hurdle, this work introduces the jMPC Toolbox, a high-level MATLAB framework for describing, tuning, simulating and generating embedded predictive controllers. Furthermore, the quad_wright and quad_mehrotra interior-point quadratic programming solvers have been developed, which are specifically tailored to solve modestly-sized online optimization problems within a model predictive controller on embedded hardware. Together, these two contributions allow an embedded predictive controller with an online optimization solver capable of over 10kHz sampling rates to be built, verified and deployed to modest embedded hardware in less than ten seconds. A case study demonstrates the effectiveness of the approach applied to an unstable, nonlinear laboratory-scale helicopter, while benchmarks against literature show for the problems of interest that the quad_mehrotra solver is the best in class. The second chosen example, steam utility systems, are designed to supply the heating, mechanical and electrical demands of an on-site process system, such as an oil refinery, paper mill, chemical process plant or a variety of other energy intensive industries. Steam is used as the working fluid within the utility system, and is generated by boilers or recovered from waste heat, which is then used to supply the…