Annual Report 2021

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Understanding and cutting the carbon footprint of milk production requires in-depth knowledge about true greenhouse gas emissions from dairy farming. To this end, the University of Helsinki and Valio have turned their focus to studying nitrous oxide. It contributes significantly to dairy production emissions but has largely been overshadowed in research by carbon dioxide.

The carbon footprint of food production is an important current topic of debate both nationally and internationally. UN estimates are that the agri-food system accounts for over 30 per cent of greenhouse gas emissions from human activities. Milk production is one part of this system. Valio has for years been doing research to establish exactly what kind of emissions are released from milk production and what could be done to them. But the main focus has long been on carbon dioxide.

Less interest has been given to nitrous oxide emissions, even though they are known to account for up to 30 per cent of milk’s carbon footprint. The University of Helsinki and Valio are working in a joint project to shed light specifically on nitrous oxide, which is commonly known by its chemical formula N2O. It is a potent greenhouse gas that is emitted especially from fertilized soil, such as grassland that is used to feed cattle.

“Nitrous oxide emissions are unpredictable and episodic, and therefore modelling these emissions is extremely challenging. Measurement requires expensive instrumentation, which is why N2O emissions have been measured less frequently than carbon dioxide emissions,” says Mari Pihlatie, professor of environmental soil science and principal investigator in the project. She has been studying nitrous oxide emissions her entire career.

The project is set to produce completely new information about nitrous oxide emissions, based on regular measurements conducted at the SMEAR-Agri station in connection with the University of Helsinki Viikki campus and at Valio grassland farms across the country.

“There’s a scarcity of research on emissions in northern environments, even though the climate here is completely unique. This project will allow us to show how our short growing season and topsoil freezing and thawing impact emissions. At the moment this information is not available anywhere.”

Current estimates of nitrous oxide emissions from grassland production are based on the IPCC emission coefficient, which has been created on the basis of the existing research evidence. Because northern regions are underrepresented in earlier research it is hard to tell whether the coefficient accurately reflects the real situation on Nordic farms. Pihlatie and her colleagues are working to verify the data. 

In addition to Valio and the University of Helsinki, the project involves a wide variety of other partners: dairy farms across Finland; the Finnish Meteorological Institute; Natural Resources Institute Finland; Yara Finland; Vaisala; Datasense; and Soil Scout. Combining the expertise of these different organizations will help to discover new ways of reducing nitrous oxide emissions. Potential new solutions include new types of fertilizer and optimizing the timing of fertilizer application in order to minimize emission levels. 

The project team have been keen to ensure that the information they produce will also have international significance. All measurements are conducted using high-quality instrumentation. Apart from nitrous oxide emissions and methane and carbon dioxide emissions, the team are looking at critical environmental factors such as soil nitrogen content, soil humidity and temperature. “This produces valuable data for the modelling of nitrous oxide emissions and ultimately for assessing the climate impact of grasslands.”

The academic goal is to report the results in the most prestigious publication series. However, Pihlatie is well aware that two years will do little more than scratch the surface of the subject – the research must be continued for much longer. Indeed, plans are already in place to apply for additional funding in the coming winter when the first measurement results are received.

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Electricity prices have reached historical highs during the past spring. Improving energy efficiency is therefore a timely step towards greater climate and economic sustainability. Tampere University and Danfoss are working closely to develop a solution that can help achieve significant energy savings in industrial machinery.

Energy efficiency is one of the fastest and cheapest ways to reduce carbon dioxide emissions. For cost reasons alone, improving energy efficiency is of great interest to both businesses and private citizens because the sanctions imposed on Russia in response to its invasion of Ukraine are limiting access to energy resources and driving up prices.

The International Energy Agency’s Net Zero by 2050 Scenario has set the target of reaching 35 per cent cuts in energy intensity by 2030. In other words, the growing population on the planet should secure the energy it needs with 35 per cent greater efficiency. The changes are driven by accelerating electrification in society, changes in consumer behaviour and improvements in energy efficiency.

Energy efficiency is a particularly critical concern in industries that use big machinery. Almost one-quarter of all electric industrial motors use variable speed drives (VSD), which control the motors by adjusting the frequency and voltage of the power supply. Various kinds of fans and pumps are typical examples of devices that make use of VSDs. VSDs improve energy efficiency as such because they are used to regulate the motor’s energy consumption.

But VSDs themselves have so far not operated at their maximum efficiency. VSDs can account for up to half of all the energy consumed. There is room to improve the energy efficiency as well as production and operating costs. Researchers from Tampere University and Danfoss are now working closely to develop a solution that would reduce power and energy losses in VSDs.

Mattia Rossi, postdoctoral researcher on the project from Tampere University, explains: “Traditionally VSD control methods are not particularly suited to deal with the multiple needs of modern industry applications. The new method we’re developing helps to minimize the power losses and extract every bit of energy available. This means we are getting more power and higher device efficiency.”

The project team’s results confirm that this advanced control method based on model predictive control (MPC) is highly effective. The first tests have produced savings of 1–2 per cent. “Savings of just one per cent are significant when we’re dealing with industrial machinery in kilowatt range,” Rossi continues. Besides, he points out, these are just the first preliminary results. The full power of the new solution will become clear when the project moves on from the current simulation stage to real industrial scale trial runs.

The opportunity to conduct laboratory trials on industry hardware is one of the best sides of industry-academia cooperation, Rossi says: “This research would not be possible without an industrial partner. The collaboration helps to align the research with market requirements. Without a business partner, we would not have such validation part which is necessary to drive our software development.”

The research team will have access to the first trial results during 2022, and the project will be completed in 2023. By then it will become clear just how big a revolution they will be achieving in energy efficiency. If they are successful in optimizing VSD energy consumption, the technology will have extensive application prospects in many industry branches. The solution developed in the project may also have practical application in energy storage and battery technology, for instance.

The project team’s goals are far-reaching: “When we come to wrap up this project we will hopefully have produced not just research but also a finalized software feature that can be further patented and potentially commercialized. We can then look ahead to improving energy efficiency in other energy branches as well.”

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The coronavirus vaccine brought home to many people just how much the efficiency of pharmaceutical product development can be improved if scientists work more closely with pharmaceutical companies. The University of Helsinki and Orion have launched a joint project to develop a databank that uses AI and machine learning algorithms and that will greatly speed up the work of chemists.

Pharmaceutical drug development is a slow process. From the initial idea, it takes at least 10–12 years to get the final product on the shelf. There are several reasons for this, starting from the slow funding mechanisms for pharmaceutical research and the expertise required by synthesis technology to complex pharmaceutical licensing procedures. A joint research venture between Orion Pharma and the University of Helsinki is now focused on speeding up one crucial stage in this process: laboratory studies on chemical synthesis reactions. To this end, the team have turned to AI and machine learning.

The development of a new pharmaceutical substance can require the production of thousands of new chemical compounds. Each and every one of them has to be manufactured separately using a new synthesis route, or cluster of chemical reactions. Each reaction has its own reagents, catalysts, solvents, temperatures and volumes. Given the large number of variables, there are also countless potential outcomes.

“Research into chemical reactions today relies largely on the professional expertise and experience of chemists,” says Toni Metsänen, senior researcher at Orion. “Chemists are inclined to use the synthesis routes that they’re already familiar with. This gives greater certainty of a successful outcome and ensures high efficiency,” he continues. Without prior knowledge of the reactions, the alternative is to turn to the literature and earlier research. But that is a slow route.

The project between the University of Helsinki and Orion is aimed at digitalizing the tacit knowledge possessed by chemists and the body of extant research and on this basis to create a rich reaction database that provides immediate access to critical information.

With reaction data readily available, optimization of a certain synthesis route will happen far more quickly – which in turn will translate into greater efficiency in the development of new pharmaceutical substances. Chemists can put more of their effort into work that counts the most and minimize loss of time.

Artificial intelligence plays a key role in medical development in all pharmaceutical companies. “Companies will typically aim to optimize a certain synthesis route so that it’s as quick and easy as possible for them to produce tonnes of a certain medicinal substance. 

In this research project we’re taking a somewhat wider view on what AI can offer and trying to find answers to completely new chemical reactions,” Metsänen explains. In other words, AI could predict what is going to happen when a certain previously used reagent is replaced by a new one or when there are changes to some other part of the synthesis route.

Toni Metsänen says the ongoing project is highly significant because it involves three different university departments: chemistry, pharmacy and computer science. What is more, the team have two full years to concentrate on their research, a rare opportunity indeed for anyone working in industry. “We will also be gaining access to special expertise in AI that  would be very hard for us to develop within the company.”

Apart from the professors working on the project, the team includes a postdoc researcher and a Master’s student. Both of them will accumulate great expertise in the project that will be in high demand in the future. “Chemists of the future will need to know how to make use of machine learning in support of the work that is done in the lab,” Metsänen says.

The project has now moved on to a phase where a significant amount of existing data has been uploaded into the software and tested. The project’s basic idea is up and running, but Metsänen is keen to stress the importance of speed: the aim is to be able to access the information required in a matter of seconds. “We don’t want to develop a separate software suite but a tool that can be adopted as part of chemists’ existing toolbox. The value of the end result will be measured in terms of ease of use.”