The Bioenergy Imperative

Author:
Avnet Abacus, Technical Marketing Team

Date
03/20/2024

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Investigating the advantages and trade-offs required to add more biofuels to our renewable fuel mix

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Figure 1: Biomass is expected to form an important part of the energy mix in 2050 (copyright International Renewable Energy Agency (IRENA))

­Today’s fossil fuels are essentially ancient solar energy trapped in dead plant matter, compressed under rocks for thousands of years, and transformed into concentrated fuels whose embodied energy can now be released through burning or other chemical processes. But what if we could radically accelerate the ‘sunlight to fuel’ process and use contemporary crops to displace the increasingly unfavored fossil fuels? That’s what the bioenergy sector of the renewable energy industry aims to achieve. However, this transition involves multiple trade-offs and has led to the discovery of numerous unintended consequences. Moreover, it is a transition that may soon face limitations due to its own success.

To provide some context, the International Energy Agency (IEA) reports that the renewables sector is expected to become the largest source of global electricity generation by early 2025, surpassing coal. By 2027, renewables will account for over 90% of global electricity capacity growth. Biofuel demand is projected to grow by 22% during this period. This increase will be driven by a rise in the use of biodiesel in the US, Canada, Brazil, Indonesia, and India; a 35-fold increase in the use of biojet fuel since 2021; and the adoption of ethanol and biodiesel in emerging economies.

The following diagram (Figure 1), sourced from the International Renewable Energy Agency (IRENA), provides an illustration of the challenge the world faces in achieving net-zero emissions by 2050, and the role that bioenergy, created using biomass, is expected to play.

Types of Bioenergy

Terminology is important for understanding this renewable sector. The US Department of Energy’s Bioenergy Technologies Office provides a set of useful definitions, as follows:

Biomass refers to a renewable energy resource derived from plant- and algae-based materials, which can include crop wastes, forest residues, purpose-grown grasses, woody energy crops, microalgae, and food waste. Biomass can be converted into liquid fuels that resemble gasoline, jet fuel, and diesel.

Biofuels encompass cellulosic ethanol, biodiesel, and renewable alternatives to hydrocarbon fuels. Biofuels are especially valuable because they are renewable, can seamlessly replace conventional fuels, and contribute to reducing the carbon intensity of various forms of transportation.

Biopower technologies involve converting biomass into heat and electricity. There are three primary methods for utilising biomass to produce biopower: combustion, bacterial decay, or conversion into gas or liquid fuel. Biopower can help to reduce the carbon intensity of electricity generation and is also ‘dispatchable,’ meaning it can be easily started up or shut down in response to the electricity grid's demand.

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Figure 2: Growth in bioenergy electricity generation, by biofuel type (IRENA)

 

Biomass can also be used to make bioproductslike those usually made from feedstocks such as petroleum and natural gas. These include plastics, lubricants, industrial chemicals, and more. As in petrol refineries, bio-refineries can make bioproducts alongside biofuels. This makes more efficient use of biomass resources as well as offsetting the costs of biorefinery operations, reducing biofuel costs and so accelerating their uptake.

The Supply-Chain Challenge

The increasing adoption of bioenergy is likely to create unintended consequences. For example, bioethanol and biogas were initially introduced on a large scale as alternatives to fossil fuels in transportation applications. It didn't take long before people started questioning the lifecycle benefits of these first-generation fuels in terms of carbon emissions, considering the energy costs involved in producing the necessary fertilisers. Additionally, there were increases in food costs in some regions as agricultural land was repurposed to grow bioenergy feedstocks.

Since then, developments have been made, and the IEA now predicts that waste and residues will fuel one-third of new biofuel production by 2027. This is, in part, attributed to policies, especially in the US and Europe, that favour fuels with lower greenhouse gas emissions - such as those produced from waste streams. In Europe, this particularly applies to renewable diesel and biojet fuels.

However, the rapid adoption of bioenergy and biofuels is putting strain on supply chains. The IEA forecasts that the demand for waste and residue oils and fats is expected to nearly outstrip the supply of the most readily available sources by 2027. Supply constraints are also compelling biodiesel, renewable diesel, and biojet producers to turn to conventional vegetable oils, such as soybean oil and rapeseed oil.

The Role of AI and ML

As the information above indicates, there is still plenty of room for discovery, development, and optimization in all forms of bioenergy. At a workshop convened in August 2022 by the US Department of Energy, bioenergy researchers outlined ways in which they believed they could leverage machine learning (ML) and other artificial intelligence (AI) strategies to accelerate their work. The consensus was that AI and ML techniques, coupled with automated experimentation systems, could help address some of the key challenges in bioenergy. These might include finding ways to engineer microbes or communities of microbes to meet specific criteria, developing closed-loop autonomous design and control capabilities for biosystems, and creating improved techniques for scaling up and automating successful bioenergy research outcomes to production status.

Among the obstacles to achieving these goals are a lack of high-quality, labelled data from which ML systems can learn, the absence of AI and ML tools tailored to bioenergy applications, and the challenge of automating experimentation systems and making them more autonomous. As is often the case at the forefront of research, there is also a shortage of individuals trained in these new techniques.

Innovation opportunities

Increasing the adoption of bioenergy will involve developing and deploying many of the same types of electronic systems used in other forms of renewable energy generation, as well as a few that are specifically adapted to the challenges of bioenergy development.

For instance, advanced control systems will be essential for optimising the performance, efficiency, and safety of bioenergy plants. These systems will require microcontrollers, sensors, actuators, and communication modules to monitor and control processes such as feedstock handling, combustion, gasification, and emissions control.

Power electronics will play a crucial role in bioenergy generation schemes. They will be needed to convert DC outputs into AC for direct use or for feeding into the grid, synchronisation circuits to align bio-generated AC with the grid phase, and transformers to adjust voltages as needed. Power electronics will also be integral in maximising the round-trip efficiency of large-scale storage systems for bioelectricity.

Automation and robotics may play a more significant role in bioenergy than in other forms of renewable energy generation. As suggested at the AI workshop described earlier, researchers need access to highly automated experimentation systems to explore a broader range of bioenergy options. Automation and robotics systems will improve the efficiency, safety, and reliability of bio-power plants by automating tasks such as feedstock handling, ash removal, and maintenance.

Similarly, bioenergy research will depend on the use of a wide array of sensor types to provide the rich datasets necessary for training ML systems to draw useful conclusions. Once sources and processes have been validated, full-scale bioenergy production plants will require sophisticated data acquisition systems that rely on a diverse set of sensors to collect, process, and analyse data from bioenergy systems.

All these data flows will originate from signals captured by rugged sensors, which will be conditioned and digitised for recording in data loggers, and then communicated, analysed, stored (and backed up) to enable the optimization of operations, maintenance, and decision-making through advanced analytics and ML algorithms.

The challenge of achieving net zero by 2050 can only be met through rapid innovation, development, optimization, and iteration. Avnet Abacus possesses the expertise, insights, tools, and components to assist electronics engineers in contributing effectively.

Avnet Abacus

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