# Developments, Trends, and Challenges in Optimization of Ship Energy Systems
## Appendix A Main Characteristics of Journal Articles on Optimization of Ship Energy Systems
(2015) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] _Applied Energy_**2015. ** System: PV, DG set, and battery on an oil tanker. Electricity production only (propulsion by steam turbine). Design optimization (sizing of PV and battery, while the power of the DG sets predetermined so that it covers the loads completely). Five operating states (electric load) are considered. Biobjective: minimize total cost and minimize CO\({}_{2}\) emissions. Algorithm: Multiobjective Particle Swarm Optimization (MOPSO) combined with elitist Nondominated Sorting Genetic Algorithm (NSGA-II). * [PERSON] et al. (2015) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] _Energy Conversion and Management_**2015. ** System: ORC operating on low-temperature waste heat of main engines of an LNG carrier. Design optimization of each of the alternative configurations and selection of the best one. Objective: maximization of the net power output. Method: HEATSEP for the heat exchangers part. Software: MATLAB Simulink and fmincon MATLAB function with SQP. * [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON] _Journal of Marine Science and Technology_**2015. ** System: a set of DG sets (diesi-electric machinery). Synthesis design and operation optimization considering lifetime of 20 years, divided in time periods and operational states. The number (configuration), the nominal power, and the load of each DG set throughout the lifetime are determined. Objective function: minimize (investment + operation costs). NOx tax is included, if applicable. Algorithm: commercial solver by means of the branch and bound technique. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] _Energy Conversion and Management_**2016. ** System: four diesel engines connected to two propeller shafts, four diesel-generator sets, exhaust gas boilers, two auxiliary boilers, and one compression chiller of a cruise ship. Operation optimization. In addition to the current system, the installation of a shaft generator/motor on each of the two propeller shafts is studied. Objective: minimization of fuel consumption. The engines are grouped in engines of equal size and performance. In each group, the operating engines are equally loaded. Independent variables: the number of operating engines in each group (integer) and the total load of each group (continuous). Algorithm: the problem is of MINLP. SQP of MATLAB is used for the nonlinear part and a branch-and-bound method is applied for handling the integer variables. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] _Journal of Engineering for the Maritime Environment_**2016. ** System: WHR system consisting of a power turbine and double-pressure Rankine cycle of a Suezmax crude oil tanker. Design optimization at nominal conditions and performance evaluation at off-design conditions. Objective: maximize total efficiency (including the power of the diesel engine). Method: systematic variation of certain variables, in a range of values of each one. One variable at a time. No optimization algorithm was used. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]. _Energy Conversion and Management_**2016. ** System: molten carbonate fuel cell (MCFC) simple or combined with steam cycle cogeneration system of an offshore supply vessel. Design optimization. Objective: maximization of exergetic efficiency. Algorithm: SQP implemented in gPROMS software. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] _Transportation Research-Part D_**2016. ** System: main engine of a cruise ship on the Yangtze river. Speed optimization only in real time. Before applying optimization, wavelet neural network is used to predict wind speed and water depth for a short time ahead. Objective: minimize fuel consumption of main and auxiliary engines, as well as cost of sailing time from port A to port B, converted to equivalent fuel consumption. Method: dynamic optimization (details of algorithm are not given). * [PERSON] et al.