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This is the sixth episode of the Flush to Data podcast. We started with a discussion on PI controllers, following by some exploration into model predictive control, human issues, and data and sensor management. Thanks Ali!Episode guide:[00:00:00] Intro[00:01:06] Who is Ali Gagnon?[00:03:44] Getting into automation[00:05:41] What is AvN? [00:06:21] What is cascade control?[00:07:46] Where is Proportional-Integral (PI) control used?[00:09:21] What is VFD?[00:09:51] Should we move away from PI control?[00:11:36] Tuning PI control loops[00:15:46] How to convince the operator of introducing disturbances?[00:21:34] Tuning PI control loops (part II)[00:25:01] Intermezzo![00:29:01] Where does PI control meet its limits?[00:32:01] Should we use mechanistic models or data-driven models for model predictive control (MPC)?[00:36:51] How to communicate abstract or theoretical concepts to team members?[00:39:51] How to quantify performance of new tools or sensors without objective reference condition?[00:44:27] Will Ali lose her jobs to robots?[00:46:11] Meta-data issues, sensor issues, data management[00:54:46] What to expect beyond the data quality barrier?[00:57:01] Thank you![00:57:18] Extras [00:58:06] Short-cutting the water cycle, future challenges, and new processes[01:02:41] Digital twins: Integration with legacy systems and cyber-security[01:07:21] Learning should not end[01:08:41] Goodbye and see you soon!Quote from "Man-Computer Symbiosis" (J.R. Licklider, 1960), collected after recording, in response to question about fear for job loss?"Men will set the goals and supply the motivations, of course, at least in the early years. They will formulate hypotheses. They will ask questions. They will think of mechanisms, procedures, and models. They will remember that such-and-such a person did some possibly relevant work on a topic of interest back in 1947, or at any rate shortly after World War II, and they will have an idea in what journals it might have been published. In general, they will make approximate and fallible, but leading, contributions, and they will define criteria and serve as evaluators, judging the contributions of the equipment and guiding the general line of thought.In addition, men will handle the very-low-probability situations when such situations do actually arise. (In current man-machine systems, that is one of the human operator's most important functions. The sum of the probabilities of very-low-probability alternatives is often much too large to neglect.) Men will fill in the gaps, either in the problem solution or in the computer program, when the computer has no mode or routine that is applicable in a particular circumstance."Links:- Automation of Water Resource Recovery Facilities - WEF Manual of Practice No. 21 (4th Edition) (WEF Store is being updated at the moment, so no link sorry.) -IWA Instrumentation, Control and Automation in Wastewater Systems STR, https://www.iwapublishing.com/books/9781900222839/instrumentation-control-and-automation-wastewater-systems- Aeration, Mixing, and Energy: Bubble and Sparks, https://iwaponline.com/ebooks/book/734/Aeration-Mixing-and-Energy-Bubbles-and-Sparks- Control Blog: https://blog.opticontrols.com/ - ICA and me - a Subjective review, https://www.sciencedirect.com/science/article/abs/pii/S0043135411008487

This is the fifth episode of the Flush to Data podcast. We started with a discussion on wastewater simulation software, following by some deep dives into uncertainty. Thanks Lina!Episode guide:[00:00:00] Intro - Who is Lina Belia?[00:05:30] Wastewater engineers save more lives than doctors[00:07:40] How does a wastewater simulator look like and what can it do?[00:12:17] Uncertainty analysis in wastewater treatment practice[00:16:15] The roots of the Design and Operational Uncertainty Task Group (DOUT)[00:21:45] Uncertainty analysis is now mature and used widely[00:24:13] The main messages of the DOUT report[00:31:28] Uncertainty propagation is solved[00:33:25] Unresolved sources of uncertainty[00:36:45] Intermezzo: Deep fun[00:41:45] Can uncertainty analysis account for black swan events?[00:47:55] Instrumentation and control to account for black swan events.[00:50:20] Goodbye for now!Link to the DOUT report, published by IWA:https://www.iwapublishing.com/books/9781780401027/uncertainty-wastewater-treatment-design-and-operation 

This is the fourth episode of the Flush to Data podcast. We start of discussing open science. We venture into many related aspects such as scientific reproducibility, scientific career evaluation, software versioning, and the distinction between the scientific process and academic institutes.Hosts: Jörg Rieckermann and Kris VillezGuest: Juan Pablo Carbajal (Hochschule für Technik Rapperswil, Rapperswil, Switzerland) Links:* Juan Pablo's web page: https://sites.google.com/site/juanpicarbajal/* OSF - Open Science Framework: https://www.cos.io/our-products/osf* A publication on the scientific mission: https://dx.doi.org/10.4161%2Fcib.1.1.6285 * Open peer review: https://peercommunityin.org/ * Book "On Fact and Fraud: Cautionary Tales from the Front Lines of Science ": https://www.jstor.org/stable/j.ctt7s7j4* Goodhart's law: https://en.wikipedia.org/wiki/Goodhart's_lawEpisode guide:[00:00:19] Typical approach to sharing of scientific results today vs. new approaches[00:07:20] Is the product of science a pdf document?[00:11:15] Juan Pablo's approach to producing and sharing results[00:19:50] Pre-register your study and methods to mitigate the risk of (inadvertent) p-hacking[00:27:10] Utility of open data, FAIR principles, copyright, licenses, sensitive data[00:45:50] Open peer review[00:54:10] Evaluating scientific careers[01:04:55] Practical measures to achieve the goals of open science[01:07:40] Science is ...[01:08:35] Closing main session[01:09:10] Start bonus session[01:09:15] Is open science a sad story?[01:11:50] Publish or perish[01:15:25] Creating knowledge is easier than ever - Can open science be weaponized?[01:24:04] Why do we publish? Why do we stay in the system?

This is the bonus material to the 1st episode of the Flush to Data podcast  with Prof. Dr. Peter A. Vanrolleghem. We discuss research on particles in wastewater and Einstein's opinion on them, compliance assessment, innovation, .Links:- Peter A. Vanrolleghem's group website: https://modeleau.fsg.ulaval.ca/a-propos/accueil/- Original article describing the scanner-based settl-o-meter: https://doi.org/10.1016/0273-1223(96)00157-6- Presentation on particles:- Einstein's 2 cents on particles: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/99EO00128- The number 73 and the number 5318008: https://bigbangtheory.fandom.com/wiki/The_Alien_Parasite_HypothesisBonus track:[0:00:00] Particles [0:05:35] Einstein's 2 cents[0:08:48] Compliance assessment, fusing hardware sensors with model-based soft-sensors[0:15:12] Do regulations affect innovation?[0:16:20] The number 73[0:19:25] Particles once more - Lagoons, bubbling sediments, and unpublished tricks[0:24:40] Thanks and goodbye!

This is the 3rd episode of the Flush to Data podcast. Our guest is Prof. Dr. Peter A. Vanrolleghem. We discuss the cubEAU, experimental design, data quality, model adequacy, open hardware, and bathing in open waters.Links:- Peter A. Vanrolleghem's group website: https://modeleau.fsg.ulaval.ca/a-propos/accueil/- Original article describing the scanner-based settl-o-meter: https://doi.org/10.1016/0273-1223(96)00157-6- Presentation on particles:- Einstein's 2 cents on particles: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/99EO00128- The number 73 and the number 5318008: https://bigbangtheory.fandom.com/wiki/The_Alien_Parasite_Hypothesis[0:00:00] Intro[0:01:20] The cubEAU - a structure to manage scientific activity as a function of challenges, methodology, and application area.[0:10:10] Experimental design - A generally applicable tool[0:15:05] Ongoing issues with online sensors.[0:18:15] Adding stochastic variations into models to account for information contained in new data sources[0:25:35] The value of field work and data collection[0:26:15] Intermezzo[0:30:32] Monitoring environmental systems[0:35:11] Open hardware, open software, patents, licensing[0:39:10] Back to sensors[0:42:25] Bathing in the St-Lawrence river[0:47:45] Thanks and goodbye!

This is the the 2nd episode of the Flush to Data podcast. We discuss micro-pollutants, wastewater sampling methods, sewer epidemiology.Hosts: Jörg Rieckermann and Kris VillezGuest: Christoph OrtLinks:- Christoph Ort: https://www.eawag.ch/en/aboutus/portrait/organisation/staff/profile/christoph-ort/show/- MS2Field: https://www.eawag.ch/ms2field/Episode guide:[0:00:00] Intro[0:01:27] Selecting a PhD topic[0:05:48] Monitoring micro-pollutant concentrations[0:10:05] Model to describe micro-pollutant concentrations[0:12:05] From dish-washer tracer experiment to system model[0:17:25] Are stochastic process models describing anomalies?[0:25:55] Tracking micro-pollutants in sewers[0:38:02] Intermezzo[0:40:38] Sewer epidemiology today[0:45:15] Sewer epidemiology in 10 years[0:51:30] Sewer epidemiology for the Covid-19 case[1:00:20] Thank you and goodbye!Bonus material[1:01:24] A researcher's condition, maximizing the value of coincidences in research[1:05:52] Self-discipline and saying no[1:08:46] The sewer ball[1:13:42] Thank you and goodbye!

This is the first episode of the Flush to Data podcast. We start with a discussion on mechanistic modelling and machine learning and venture into models for emulation, uncertainty quantification, and data quality. Bonus material includes a discussion on aspects of current scientific practice, including the lack of hypothesis testing, the evaluation of novelty, and the challenges with a generalist approach.Hosts: Jörg Rieckermann and Kris VillezGuest: Juan Pablo CarbjalLinks:* Juan Pablo's web page: https://sites.google.com/site/juanpicarbajal/* Article relating Gaussian processes and Kalman filter: www.jstor.org/stable/2984861 * BBC podcast on Gauss: https://www.bbc.co.uk/programmes/b09gbnfj* Using Lake Zurich as a heat sink: Unfortunately, we could not back-track the original source, despite considerable effort. If anyone of the listeners happens to know how to access the original source we would be grateful for a notice. The best we could find was documentation of related projects by Eawag: https://thermdis.eawag.ch/ and [1]. These show that ecological consequences have indeed been assessed in detail. * Goodhart's law: https://en.wikipedia.org/wiki/Goodhart's_law* An invitation to reproducible computational research: https://doi.org/10.1093/biostatistics/kxq028* Science in the age of selfies: https://doi.org/10.1073/pnas.1609793113 References:[1] Wüest, A. (2012). Potential zur Wärmeenergienutzung aus dem Zürichsee. Machbarkeit. Wärmeentzug (Heizen) und Einleitung von Kühlwasser. Kastanienbaum: Eawag. DORA-Link Episode guide:[0:00:00] Who is Juan Pablo Carbajal?[0:03:10] Mechanistic modelling versus artificial intelligence[0:07:08] Who is Juan Pablo Carbajal? (ctd.)[0:09:26] Cross-fertilization between robotics and wastewater engineering[0:15:05] Emulation: using models to approximate other models[0:21:22] Incorporating common sense and prior knowledge into data-driven models[0:31:31] Equivalence between Gaussian processes and Kalman filter[0:33:50] Utility of emulation[0:40:15] Utility of quantified uncertainty[0:44:50] Intermezzo[0:49:04] What can models say about data quality [1:02:15] How to communicate about data quality?[1:10:10] Preparing engineers for the future[1:15:23] Thank you and goodbye!Bonus material:[1:16:40] Interpretable machine learning models[1:22:33] Hypothesis testing[1:26:14] Critical assessment of novelty[1:30:50] Barriers to the generalist approach [1:35:48] Thank you and goodbye!

Hi!  It's Kris and Jörg and we're on fire to bring you the Flush to Data Podcast, where wastewater and stormwater meet data science. Our mission is to explore the role of data and models in the world of wastewater science and technology and to bring you experts in the various domains who share their passion and valuable insights.  This podcast is for anybody interested in recent developments on sewers, wastewater treatment plants and water courses. The die-hard wastewater professional, scientist, sewer rat or post-graduate student trying to figure out what we can learn from data on the models we use in our work. We invite experts to discuss recent developments in wastewater modeling, monitoring, data analysis, their favorite bands, and the most probable questions to the answer 42. We are a process engineer and an urban hydrologist with a passion for likelihood functions, prior distributions and music, excited to share the lessons we've learned on our journey in the universe of activated sludge and combined sewer overflows.Follow us on Twitter or talk to us on the next wastewater conference.--- Credits ---In our Trailer and intro we use the beautiful music from the "Obliterator" Amiga game by the incredible David Whittaker https://en.wikipedia.org/wiki/David_Whittaker_(video_game_composer). Also, thanks to to this FLICKR user for providing the backdrop image under a CC-BY-ND2.0 license.