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Identifying ways to make biomass a more effective alternative energy source

December 19, 2017

Worldwide demand for energy escalates every year, and the consumption of fossil fuels continues to increase despite the growing supply of alternative energy options. Globally, about 81 percent of energy comes from a finite supply of fossil fuels like oil, coal and natural gas. Fossil fuels are used to heat homes, run vehicles, power industry and manufacturing, and provide electricity.

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Researchers are looking for ways to increase the energy value of biomass. Using data analytics provides valuable insights.

Shifting the world’s consumption toward more renewable energy sources continues to be a slow transition. Renewable energy, which comes from sources that are naturally replenished – such as sunlight, wind, rain, tide, waves and geothermal heat – offers a cleaner option with less pollution and a potentially limitless supply.

Finding a cost-effective and powerful renewable energy source that can compete with fossil fuels like coal and gas is a holy grail for energy scientists.

Can biomass replace coal?

One researcher in Sweden has been looking at the viability of using biomass as an alternative energy source. In her thesis, “Off-gassing from thermally treated lignocellulosic biomass”, Eleonora Borén studied the properties of renewable energy to better understand if biomass can replace coal for producing energy.

Wood remains the largest biomass energy source today. Sources include forest residues (such as dead trees, branches and tree stumps), yard clippings, wood chips and even municipal solid waste. Biomass is often a by-product of lumber production – the unused parts of a tree after it’s been timbered.

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Wood chips are used in producing biomass fuel.

Although wood sources of biomass are plentiful in, for instance, Scandinavia, the USA and Canada, it is not efficient on an industrial scale to burn the “raw” biomass directly. Instead raw biomass must be pretreated to raise what as known as its “energy value or energy density”. This means wood chips must be processed to increase their economic value as a fuel.

Processes that raise the energy density

Eleonora Borén´s research targeted pine and spruce biomass. A variety of thermal pretreatments can be used to raise the energy value of such biomass. One of these is known as torrefaction, a thermal process that converts wood chips into a coal-like material. The process is similar to roasting coffee beans.

Torrefied biomass has better fuel characteristics than the original biomass. It is also more brittle, making grinding easier and less energy intensive. After the biomass is torrefied, it goes through a process to compact it into pellets.

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Wood pellets are more efficient as fuel than wood chips.

The energy density of pellets from thermally treated biomass approaches the energy density of the coal traditionally used in power plants. One of the positive economic aspects of pelletization is the compaction that takes place, meaning that much more energy can be stored and transported per “volume unit” than for untreated biomass.

However, there are several risks associated with the handling, storage and transportation of wood chips and pellets that make up biomass. When such biomass is, for instance, stored in large silos or closed cargo containers, there are three major risks:

  • self-heating in the middle of the storage pile of the chips/pellets
  • dust explosions
  • off-gassing

Off-gassing means the emission of permanent gases (those which remain a gaseous form under normal conditions) and volatile organic chemicals (VOC).

In other words, when storing and transporting this type of biomass, undesirable chemicals are released into the surroundings. Some chemicals can be toxic, some can cause skin irritation, or even death.

Two of the gases that can be released from both treated or untreated biomass are carbon-monoxide (CO) and carbon-dioxide (CO2). Carbon-monoxide is toxic and coupled with oxygen depletion in closed rooms or storage facilities, there is a great risk for lethal effects for humans and animals.

Determining which factors affect off-gassing and energy density

Understanding what can be done to reduce off-gassing of CO, CO2, methane, and the emission of VOCs from woody biomass was one of the driving forces underpinning Borén’s research. The other goal was to understand how to increase the energy density or economic value of the biomass.

In her research, Borén used a combination of MODDE and SIMCA from the Umetrics Suite of Data Analytics Solutions to answer these questions. She relied on Design of Experiments (DOE) to investigate the influence of parameters affecting thermal pretreatments and pelletization processes. And then she used multivariate data analysis (MVDA), including both PCA and OPLS-DA, to evaluate results from all of the off-gassing experiments.

DOE allows multiple input factors (e.g. torrefaction temperature & time, or pelletization pressure) to be manipulated to determine their affect on a desired response (properties of the resulting pellets). Process inputs can be varied at the same time allowing the interactions between variables to be understood. DOE is the most efficient methodology for understanding which process inputs affect outputs and the relationships between inputs and outputs.

In addition, using MVDA provides visualization of complex relationships between process conditions and measured off-gassing of various chemical species with colorful and informative scatter plots. You can see an example of typical score and loading plots from a PCA model in the graphic below:

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In Borén’s research, using DOE, PCA and OPLS-DA provided by the Umetrics Suite were indispensable tools for planning all experimental campaigns and for evaluating all of the resulting data.

Reaching new conclusions using data analytics

The research results demonstrated that off-gassing levels from thermally treated biomass is dependent on numerous parameters, such as feedstock, process type, process severity, post-treatment, pelletization settings, and storage conditions.

Borén’s thesis stated, “An interesting observation from the studies is that increases in the material’s moisture content in various parts of the process chain generally exacerbate off-gassing. Thermally treated biomass has been perceived to require less water-resistant storage solutions than untreated material, as it is less prone to take up moisture. However, even if it does not directly absorb moisture, water left on its surface will be carried through to downstream processing and handling steps, potentially aggravating off-gassing at various storage points.”

She also pointed out that maintaining a low and controlled storage temperature will reduce off-gassing. She said that additional precautions may be needed, such as ventilations along the production line to mitigate VOCs of concern. Her results showed how important it is to identify key exposure points along the process chains, as many elements have measurable impacts.

Multivariate data analytics provides a better understanding about which parts of the process can be changed to increase efficiency and quality, even when processes are impacted by the need to manage costs.

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Using OPLS and DOE during production processes can uncover ways to increase the energy value of biomass, such as branches, lumber waste and other forest residues, used as feedstock for biomass fuel.

Using DOE and data analytics, Borén showed that effective cooling systems are crucial to reducing off-gassing and that moisture preconditioning of in-line pelletization makes a difference. Her conclusions suggested that when cheap open-air storage solutions without full rain protection are the only options for biomass producers, attention to production optimization is especially crucial. Monitoring the entire process by measuring all outputs with data analytics can improve the final product and help reduce off-gassing of permanent gases and VOCs.

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Topics: Data Analytics, Business Metrics, Manufacturing Processes, Design of Experiments (DOE)

Lennart Eriksson

Written by Lennart Eriksson

Sr Lecturer and Principal Data Scientist at Sartorius Stedim Data Analytics