Multi Objective Optimization

 

For the multi objective optimization we look at each system’s options and chose 5 different scenarios to analyze to evaluate the best possible combination of options. In the analysis we compare each option’s emissions as well as costs and combined intervention times over the lifetime of the entire system. The data for the isolated systems can be taken from the second assignment. After setting up the 5 options the total cost and emissions result as following:

 

 

 

Different than in assignment 2 we also did an evaluation for the intervention time by option:

 

 

 

Just from the total numbers, option 4 seems to emit the least amount, while also only needing little intervention time. The total amount does not give a clear image of the best option based on multiple criteria, to do so we have to analyze the data further. We use an AHP analysis to assign weights to different criteria, to figure out the best options based on our preferences. The criteria we chose were emissions, time and cost. Emissions are very important to us, because climate change is one of society’s biggest crises right now and we want our system to be identified as environmentally friendly, while powered by renewable energy. The results are as followed:

 

The cost and Intervention time analysis both show option 5 as the best. That means if our priorities are only minimizing the duration of interventions and expenses, option 5 would be recommended. 

 

For the emissions pie chart, the option shown as the second best is option number 2, while also achieving the highest total emissions. Therefore further adjustments of the priorities are necessary. Still, while considering the least polluting option, number 3 came out as superior, which confirms the expected result. Therefore option 3 is chosen as our design option, because it fulfills our most important criteria. 

 

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