Hafner Consulting LLC
I work on science and engineering problems in a variety of areas related to the environment, and have been doing consulting work since 2012. Feel free to browse projects and papers, or contact me with questions, to join a mailing list, or to discuss a potential project. I'm also active on ResearchGate, GitHub, and LinkedIn.
- Sasha D. Hafner, Ph.D.
Manure storage temperature model STM (November 2021)
Stored animal manure is a source of ammonia, methane, and other compounds, and emission rates are strongly temperature-dependent. Estimating emission and assessing mitigation practices can therefore benefit from a simple model for predicting manure temperature. The STM model does this: https://github.com/sashahafner/STM! Unlike most of my work, which is implemented in R, STM is a very fast Fortran program, available as a stand-alone executable (executable files can be downloaded for Windows or Linux, or users can compiled the code themselves).
The plot below shows measured manure temperature (red), model predictions (black), and measured air temperature (blue) for a round concrete storage tank.
Update of emission factors for field-applied manure based on the ALFAM2 model (October 2021)
Ammonia volatilization from field-applied manure contributes to air pollution throughout the world, and to the loss of valuable fixed nitrogen. The Danish Ministry of Environment recently commissioned a review and update of emission factors for field-applied manure based on the ALFAM2 model. You can check out the new emission factors, which include low-emission approaches such as injection and acidification, and our methods in the final report we recently completed: https://pure.au.dk/portal/files/223538048/EFreport23092021.pdf.
New microbial model on methane emission from stored manure (June 2021)
The open-source ABM model is available in the ABM R package at https://github.com/sashahafner/ABM. Send any feedback or feature requests (anyone interested in a web interface?) through the Issues page.
You can find a detailed description of the model in our new open-access paper in PLOS ONE: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0252881
The original focus was stored waste, which is a significant source of greenhouse gas emissions, and we hope the new model is a useful tool for understanding, predicting, and ultimately reducing methane emissions that contribute to climatechange. But this flexible model can be applied to biogas reactors as well.
Thanks to co-authors Frederik Dalby, Sven G. Sommer, and Søren O. Petersen
Introduction to practical data analysis for graduate students (June 2021)
Check out my new short book "A Crash Course in Practical Data Analysis". Download for free from: https://github.com/sashahafner/CCPDA
This is an irreverent introduction with advice, explanations, demos, and some opinions. It's meant to help graduate students in science and engineering improve their data analysis skills by explaining fundamental concepts and practical steps for accurate and useful data analysis.
Share any feedback or requests through the Discussions page: https://github.com/sashahafner/CCPDA/discussions
New BMP test planning tool in OBA (Janurary 2021)
The free web app OBA has a new tab that can help users select inoculum and substrate quantities that meet current recommendations, and checks inputs and outputs to help avoid common mistakes. Find OBA at https://biotransformers.shinyapps.io/oba1.
Additions to biogas web app OBA (October 2020)
I have made some significant additions to OBA recently, including an option to apply the Standard BMP Methods validation criteria, and to predict BMP of food products based on nutrition facts (based on Konrad Koch's great idea for alternative positive controls--see paper 46). Find OBA at https://biotransformers.shinyapps.io/oba1. For regular updates, send a message to join the mailing list.
International Inter-laboratory Study on BMP (IIS-BMP): final results available (June 2020)
Check out the results from a large international effort to standardize measurement of biochemical methane potential (BMP) in a new open-access paper.
This project, funded by the Swiss Federal Office of Energy, cut through the muddle of inaccurate BMP testing through generation and careful analysis of a large and detailed inter-laboratory dataset. The recommendations that came out of this work are a central component of the new Standard BMP Methods website, and have the potential to substantially improve inter-laboratory reproducibility.
New simple (but accurate) method for measurement of biochemical methane potential (BMP) (November 2019)
This method has been in the works for years (I first started working on the concept during my PhD at Cornell in Bill Jewell's lab), but was always on a back burner. Some hard-working MSc students at Aarhus University and collaborators at TUM, UQ, and DBFZ helped make it happen. With this new gas density BMP (GD-BMP) method, BMP can be be accurately measured with even the most basic laboratory equipment.
The method has now been tested in three labs and compared to measurements made in two others using a state-of-the-art method (AMPTS II). For details, see this open-access paper. To use the method, check out the detailed documents on measurements and calculations on the Standard BMP Methods website (available in English and Spanish).