Frank Neffke, Complexity Science Hub, Vienna
with Matte Hartog, Andrés Gómez-Liévano, Ricardo Hausmann
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.
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
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