Course description
In a resource-constrained world where greenhouse gas emissions are becoming a more important issue every day, shipping needs to move forward to more efficient and sustainable ships.
This course gives an introduction of different methods and tools to be used for optimization of ship energy systems, from the overall design of the ship until the energy management system used in ship’s operations.
The course will the divided in 5 lectures, each taking one of the five days of course:
- Energy efficiency in shipping: some background: This lecture will set the ground for the coming days, providing some background information about why we need energy efficiency in shipping, what are the current trends and state-of-the-art technologies, as well as new developments. In this lecture I will also give some theoretical background (boring, I know, but it’s needed) to some specific principles that we will need to know for the later parts, such as process integration and pinch analysis.
- An introduction to optimization: Part 1, linear programming: Surprise surprise, this lecture will deal with linear programming, one of the most well known and reliable means for optimization. Similarly to all the following three lectures, this will be subdivided in half: 2 hours of theoretical background about linear optimization, and 2 hours of practical application.
- Data-driven modelling and optimization: How can we have a course today without taking the “Big Data” approach into consideration? Our guest lecturer Andrea Coraddu will speak about machine learning in general, how to create appropriate ship models based on available measurements, and on how to use them to improve the system performance
- An introduction to optimization: Part 2, nonlinear programming: Second round in hardcore optimization, we deal now with those cases when our beloved linear programming doesn’t work (or simply might not be the most obvious choice). Standard nonlinear programming will be integrated with heuristic algorithms (such as genetic algorithms and particle swarm optimization), again leaving much room for testing and exercises
- Optimization of ship operations: Here we focus specifically on ship operations. How can we improve a system that has already been built? How do we take into account how the system will be operated when we are designing it? Dynamic programming, linear programming (again!) and model predictive control will be our tools for this task.