Inventing modern invention: the professionalization of technological progress in the US | 9am Thu Jan 30, 2025

[Grigory Bronevetsky]

Modeling Talks

Inventing modern invention: the professionalization of technological progress in the US Frank Neffke, Complexity Science Hub, Vienna Tues, Jan 30, 2025 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/inventing-modern-invention-the-professionalization-of-technological-progre More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home Abstract: Between the mid-19th and mid-20th century, the US transformed from an agricultural economy to the frontier in science, technology and industry. We study how the US transitioned from traditional craftsmanship-based to today's science-based innovation. To do so, we digitize half a million pages of patent yearbooks that describe inventors, organizations and technologies on over 1.6M patents to which we add demographic information from US census records and information on corporate research activities from large-scale repeated surveys on industrial research labs. Starting in 1920, the 19th-century craftsmanship-based invention was, within just 20 years, overtaken by a rapidly emerging new system based on teamwork and a new specialist class of inventors, engineers. This new system relied on an organizational innovation: industrial research labs. These labs supported high-skill teamwork, replacing collaborations within families with professional ties in firms and industrial research labs. This shift had wide-ranging consequences. It not only altered the division of labor in invention, but also reshaped the geography of innovation, reestablishing large cities as epicenters of technological progress and introduced new barriers to patenting for women and foreign-born inventors. Bio: Frank Neffke leads the interdisciplinary Science of Cities/Transforming Economies research program at the Complexity Science Hub (CSH) in Vienna (Austria). This program aims to understand how economies learn, regarding them as complex systems that first distribute and then coordinate collective knowledge across individuals, firms, regions and countries. Before joining CSH, Frank served as the Research Director of the Growth Lab at the Harvard Kennedy School. His research focuses on economic transformation and growth. He has written on a variety of topics, such as structural transformation and new growth paths in regional economies, economic complexity, division of labor and teams, the consequences of job displacement and the future of work.

[Grigory Bronevetsky]

Video: https://www.youtube.com/watch?v=s7p9QcUK7fQ

Slides: https://drive.google.com/file/d/1vfSpVe_kQhcHjCrviau2N7MV3dusbPuU/view?usp=sharing

Summary:

• Focus: energy generation using renewable resources

• Many options and choices:

  • Electrify vs liquid field

  • Biofuel pathway options

  • Solar power via electricity, liquid fuels, etc.

• Optimization-based process synthesis

  • Traditional process synthesis

    • Hiearchical design

      • Reaction

      • Separation & recycle

      • Heat recovery network

      • Industrial utilities

    • Sequential decisions, optimized one at a time

  • Optimization-based design / network design

    • Solving constraint system that related different industrial components

  • Aiming to provide feedback to experimental chemists/process engineers to identify regions of design space that are most/least fruitful

  • Simulation

    • Input chemicals/economics

    • Model of chemistry

    • Design

    • ->Output

  • Optimization: find design that optimizes the target “reward” function

  • Their approach: solve system of equations where design is embedded

• BECCS: Bioenergy with Carbon Capture and Storage

  • Use biomass to generate energy / liquid fuels for energy storage, long distance energy transport

  • Emits CO2 in the process

  • Can capture at different points in the industrial process

    • Upto 75% can be captured with sufficient work to capture

  • Emissions due to transporting biomass to facility

  • Tradeoff

    • Larger facilities are more efficient at producing energy/fuels but transportation costs/emissions are higher

    • Smaller facilities have lower transport costs but are less efficient

    • Hybrid: intermediate facilities that densify feedstock

  • Can optimize system design around industrial constraints and revenues 

    • Carbon credits due to capture

    • Ethanol price

    • Biomaterial sources (they focused on switchgrass grown on marginal lands but can expand to agricultural residue, forests, etc.)

  • Range of technological options

    • Gasification -> Electricity or H2

    • Fermentation

    • Pyrolysis

    • Capture in Flue, Biogas

    • Key to document the supply chain: 

      • Emissions: harvesting, transportation, etc. 

      • Capture: soils

      • Impacts of outputs:

        • Liquid fuels are burned: emissions

        • Electricity output can displace CO2 emitting power

• Integrated Spatially Explicit Network model

  • Typically supply chain analyses focus on point solutions that ignore geography

  • Biomaterial availability varies both spatially and temporally

    • Different types of lands where different amounts of material can be grown

    • Biomaterial harvest varies over the year and yields vary across years

  • Optimizing facility design in a spatially-explicit way

  • A field-to-product analysis

    • Bioenergy lands

    • High quality land appropriate for food crops

    • Low quality lands best for grazing

    • Middle-quality lands good for bioenergy crops

    • ML and drone tech being used to identify marginal lands in the US

      • atlas.glbrc.org

      • Intermittent and recently abandoned land

      • Formerly irrigated land

    • Estimate the productivity of various lands: 

      • SALUS model: https://www.cibotechnologies.com/salus-model/

      • Yield maps for different crops

      • Distribution of yields for a given crop/land

      • 10 weather/yield samples

    • Focus on switchgrass in US Midwest

  • Can analyse operating efficiency of biorefineries at any location, given material availability in any given location

  • Large-scale optimization of deployment of refineries across a large area

    • Stochastic Mixed-integer Program

    • Can do single-state analysis that places a biorefinery and densification sites

• Investigation of Economic and Environmental Tradeoffs

  • Larger-scale analysis of 8 US Midwest states

  • Choosing from among a fixed set of sites for biorefineries, sequestration, depots

    • Constrained by geological structure, availability of rail transport

    • Accounting for price and emissions of electricity from regional grid

      • Need electricity for some types of sequestration (where heat biorefinery is not enough)

  • Goal is to compute optimal design, which informs decision makers about the regions of design space where more detailed investments should be explored

  • Optimal configuration depends on the types of emissions for which carbon credits are paid

    • If emissions in transport, harvest, electricity displacement are accounted, explicitly optimize location to be close to biomass, located in high emission grids