Chemical & Biological Engineering Research
Theory, Data Science and Systems
In recent years, the digital age has transformed chemical engineering—and vice versa—with an increasing use of “big data” and data science. When paired with theoretical principles, such as thermodynamics and transport phenomena, we can use big data and data science to further explore chemical engineering for research and industry applications.
What is big data?
Big data refers to massive and complex data sets that tend to grow rapidly and usually cannot be handled by traditional database systems. Big data sets offer additional insights with large statistical impacts, but it also comes with greater challenges and unique complexity to consider.
What is chemical engineering data science?
Integrate mathematics and computational methods, such as machine learning and artificial intelligence, to analyze and harness data for applications in chemical and systems engineering.
With computational visualizations and actionable, data-driven insights, use data science and chemical engineering theory to:
- Improve efficiency, safety, and quality in industrial processes.
- Calculate and predict reaction pathways for sustainable, greener energy.
- Transform raw data from sensors to improve insights from experimental research.
- Predict molecular or system properties for material and pharmaceutical development, safety and stabilization checks, separation techniques, and more.
What is chemical systems engineering?
Design, optimize, and manage large and complex systems by combining engineering, business management and practical requirements, like energy or user needs. System application can range from physical systems, such as energy plants or waste management systems, to conceptual systems, like software frameworks or algorithms. At times, it can be simple as helping optimize a factory floor, or weighing cost and risk factors. Other times, it goes beyond a traditional manufacturing system, analyzing what problems need resolving, predicting failures or delays, and finding solutions to all of these problems.
What will you do in chemical systems engineering?
Systems engineering emphasizes holistic thinking and considered the full lifecycle of a system, including:
Product design, testing and creation
Waste and environmental impact
Worker training and safety
Cost, shipping and scheduling
Energy requirements and sustainability
A closer look. What are some focus areas?
Computer and information research science is projected to grow 20% through 2034 according to the U.S. Bureau of Labor Statistics.
Our researchers and alumni expertly apply computational tools and data science to solve complex challenges in chemical engineering. Learn about some of the methods they utilize below!
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A subset of artificial intelligence, machine learning (ML) enables computers to learn from large data sets to make data-driven predictions and decisions, without being explicitly programmed for every scenario. With ML, we can train predictive models to screen for the best solvents for plastics recycling, design nanoparticles for drug delivery systems, detect toxic forever chemicals in water, and find new energy-efficient catalysts for chemical production.
Study and solve complex chemical problems using computer simulations, mathematical modeling and theoretical methods. With computational chemistry, we can predict catalytic and material properties, model reactions turning agriculture waste into hydrogen and fuel, and accelerate the discovery of sustainable electrolytes for batteries.
Using chemical theories and principles to calculate force, reaction times, and motion of atoms and molecules, a computer can apply statistics, probability, and algorithms to predict and simulate molecular systems. With this information, we can optimize and assess the stability of proteins for drug design, analyze DNA to predict biological functions, study characteristics and properties to fashion new materials, test structure for nanoscale technology, and more.
Create data-driven visual or mathematical representations of workflows to identify bottlenecks, inefficiencies and opportunities for improvement. With this information, we can structure processes and workflows to enhance performance, reduce costs, which can save valuable energy resources, make the workplace safer, mitigate environmental harm and waste disposal.
Use mathematical principles and algorithms to simulate, analyze and solve complex problems involving fluid mechanics and flow of liquids and gases. Not only does this help us explore innovative applications of fluid mechanics research, it also reduces the need for physical testing which can be more costly and time intensive. For example, we can run simulations showing the dynamics of blood flow or drag reduction in order to study blood diseases or how drag on the molecular level affects polymer development.
Median Wage
With a Bachelor’s degree according to the Bureau of Labor Statistics in May 2024
US Dollars121,860
chemical engineering
US Dollars140,910
computer and information researcher
US Dollars109,660
health and safety engineering
New certificate builds engineers who work with AI, not around it
Amanda Smith knows the question is coming. At just about every admitted student info session at the University of Wisconsin-Madison…
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Faculty
Our interdisciplinary computational research groups collaborate and work closely with experimentalist groups, targeting data to the needs of experiments and testing theories and data-driven conclusions.
circular economy, energy, multi-level, robust and supply chain optimization
Styliana Avraamidou
Duane H. and Dorothy M. Bluemke Assistant Professor
molecular modeling and simulation, applied mathematics, ML, self-assembly
Rose Cersonsky
Conway Assistant Professor
computational dynamics
Mike Graham
Steenbock Professor, Harvey D. Spangler Professor & Vilas Distinguished Achievement Professor
Computational
Dan Klingenberg
computational chemistry, sensors
Manos Mavrikakis
James A. Dumesic Professor Vilas Distinguished Achievement Professor
AI & ML, optimization, algorithms, control & experimental design
Joel Paulson
Gerald and Louise Battist Associate Professor
process design, modeling and optimization
Ross Swaney
Associate Professor
molecular simulations, solvent effects
Reid Van Lehn
Sobota Associate Professor
John Yin
Vilas Distinguished Achievement Professor
Optimization, control, data science, energy and environmental systems
Victor Zavala
Baldovin-DaPra Professor
Affiliate faculty: Raman, Spagnolie
Research Centers and Institutes
Our faculty are leading and participating in a wide variety of interdisciplinary centers and institutes: