Is it time for AI and Data Analytics to redefine SFOC and emissions?

asks Rob Mortimer, Founder and CEO of Fuelre4m

AI is making significant inroads in the maritime sphere, enabling faster and wider ranging data analytics and streamlining tasks at sea and on shore.

As we embrace greater use of technology across the shipping industry, I think now is the time to examine some of our long-held processes and consider whether we can improve the way we calculate and analyse data in order to be more efficient and sustainable.

One area I believe is ripe for reform is the widely used and long-accepted calculation of Specific Fuel Oil Consumption (SFOC). At present I feel this is anything but specific – at best, it’s a calculated guess!

Working with Virtual Machine Software (VMS) and data analytics companies while analysing the performance of Fuelre4m’s Re4mx ‘additive’, it became clear to us that accurate measuring of fuel consumption is a thing of myth and legend, with thousands of variables being used to work out what should be a simple calculation. The maritime industry is burdening itself with a mix of measures, assessments and guesses, including: weight, volume, back to weight, temperature, assumed (indexed) energy density, weight to volume conversions, then taking into account the sea, the weather . . .

Let’s get back to basics. I say all that matters is how much fuel went into the engine to produce how big a bang to push the piston.

Fuel consumption in the engine doesn’t care about hull fouling. It doesn’t worry about speed through water vs speed over ground. It’s oblivious to tides, currents, hull depth and load. All that matters is how many grams of fuel turns into how many kW of energy. So why don’t we start with isolating what it is we want to prove?

Let’s take a battery-operated car. You charge it with electricity (fuel), and you can count how much you put in, how much is left in the tank (battery), and how far you can go, down to the smallest of measurements. Why have we not managed to get to that level of technology on a ship’s engine? Is it because we can’t, or because we don’t want to?

The whole world has been focussed on moving away from fossil fuels to the degree that the ‘problem’ of fuel consumption has been swept under the carpet, thinking that in a few years we’ll have moved away from fossil fuels. This year brought a realisation that we can’t transition to clean energy that fast.

To move forward, I would like to see engine manufacturers introducing accurate and calibrated fuel management systems as part of their ‘standard’ engine package. Things like mass flow meters shouldn’t be fitted as an afterthought, they are essential to performance proving. I’d like to see output power monitoring systems and real-time measurement too. After all, the only things we need to know, to be able to assess vessel efficiency, is the fuel efficiency of the engine.

In a diesel generator (auxiliary engine on a ship), it’s easier to calculate. Weight of fuel in (kg), minus weight of fuel out (kg), divided by kWh produced. Purists will still argue there are other conversion variables to consider, but it’s now as simple as two mass flow meters and a constant power meter across the power output. So how do we make it as simple on the main engine?

This is where AI and Data Analytics come in. Once you have the right technology, measuring the right things, fuel consumption analysis has meaning. AI can be used to spot changes in the performance of your fuel. Listening to your engine through training to accurately measure the ‘bits & bytes’ of fuel and counting how much reached the piston and how much was wasted.

This is a big subject, and I hope to be back soon examining other calculations that are ready to be reformed, such as measuring the efficiency of the fuel in the engine instead of the fuel efficiency of the vessel.

Rob Mortimer is Founder and CEO of Fuelre4m which is embracing innovation and technology to empower businesses to operate more sustainably by revolutionising the use of fossil fuels.