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Complex Systems: BBCEE

Active projects in Biomedical, Biochemical, Chemical & Environmental Engineering (BBCEE) with applications for complex systems:

Applied robust Bayesian analysis adds the dimension of probabilistic imprecision to standard model-based uncertainty analyses. The robust Bayesian approach employs sets of probability measures to describe uncertainty, rather than precise values. This concept has the potential to capture ambiguity or disagreement in probability specification in a variety of ways and therefore can provide a more realistic portrayal of scientific knowledge. The objective of our research in this area is to bring the strengths of the robust Bayesian approach to bear on the problem of separating sources of uncertainty in environmental model-based assessments.
(Faculty contact: Borsuk)

Bayesian networks of society-environment interactions are being developed to aid management and decision-making under conditions of environmental variability and uncertainty. By succinctly and effectively translating causal assertions between variables into patterns of probabilistic dependence, Bayesian networks (BNs) facilitate logical and holistic reasoning in complex systems. Such reasoning is necessary for accurate analysis, synthesis, prediction, diagnosis, and decision-making. In society-environment systems, BNs are useful because the predictive link that we want to model is often a complex causal chain, the entirety of which rarely falls within a single, coordinated research project. BNs allow this causal chain to be factored into an articulated sequence of conditional relationships, each of which can then be quantified independently using an approach suitable for the type and scale of information available.
(Faculty contact: Borsuk)

Complex fluids comprise a large class of microstructured soft materials such as emulsions, foams, gels, colloids, synthetic and bio-polymer melts and solutions, liquid crystals, etc. The study of soft matter presents scientific challenges and has important industrial links in the pharmaceutical, petroleum, plastics, food, and personal care industries, as well as a broad range of environmental (e.g., waste-water treatment) and biomedical applications (e.g., targeted drug delivery). Our research aims to understand the interrelation between the microstructure dynamics and macroscopic behavior of complex fluids with particular focus on the interplay between microstructure distortion by external forcing (flow, electric field) and microstructure relaxation by intrinsic mechanisms (e.g., drop shape relaxation, polymer recoiling). Current projects include studies of cell micro-hydrodynamics, particle migration in viscous flows, and rheology of surfactant-laden emulsions.
(Faculty contact: Vlahovska)

Process design and evaluation is under way for both current and advanced processes for biological conversion of sustainable resources into commodity products. These designs are used to evaluate economics and materials flows (e.g., feedstock demand, waste products), to identify research priorities, and to quantify the impact of process improvements in terms of both economic and environmental metrics. "Biorefineries" producing coproducts such as animal feed and electricity in combination with biologically-based production of fuels, chemicals, and materials are of particular interest.
(Faculty contact: Lynd)

Representation of model inputs and parameters using stochastic, time-dependent parameters can improve the accuracy of model predictions and representations of uncertainty. This is because, in deterministic models of environmental systems, systematic discrepancies between model simulations and measured data typically occur. These systematic deviations may be indicative of true indeterminism, or they might be the consequence of model aggregation and simplification. In either case, the implausibility of typical statistical assumptions implies that parameter uncertainty estimates or model extrapolations based on common techniques are likely to be unreliable. To address these discrepancies, we propose making selected parameters in the model time-variable by treating them as continuous-time stochastic processes. We have developed a Markov chain Monte Carlo algorithm for Bayesian estimation of such parameters jointly with the other, constant parameters of the model. The algorithm consists of Gibbs sampling between constant and time-varying parameters using a Metropolis-Hastings algorithm for each parameter type. We have tested our algorithm using a simple global climate model in which an additional stochastic forcing component is introduced. The results show that the algorithm behaves well, is computationally tractable, improves the fit of the model to the data, and provides reasonable estimates of the additional forcing component over most of the simulation period.
(Faculty contact: Borsuk)

Resource and environmental analysis facilitates the effective development and integration of industrial processes. Successful integration relies greatly upon expert analysis of relationships with respect to other industrial processes, human activities and choices, and the environment.

Analyses focus on:

  • Feedstock production and utilization
  • Process technology
  • Resource sufficiency
  • Transition dynamics
  • Policy formulation and evaluation

(Faculty contacts: Lynd, Gerngross)

Social and biological indicators of sustainability can be used to encourage individual and organizational stakeholders in a natural resource to act in ways that promote ecological, economic, and social health. We hypothesize that regular monitoring and reporting of such indicators may improve resilience in the complex human-environment system by improving stakeholder perception of ecological change, enhancing learning, and facilitating the process of adaptive management over time. This expectation arises for two reasons related to psychological framing: First, an explicit link between pollutant controls and measurable indicators will frame the sustainability issue in terms of “property rights.” Second, well-designed indicators are more easily remembered and processed and may link more easily to personal aspirations. There is evidence to suggest that both effects are likely to motivate stakeholders to act in ways that promote sustainability. We anticipate that the results of our work in this area will be used to design regulatory frameworks, especially in the energy domain, that ensure environmental protection while exploiting economic efficiencies and addressing social justice concerns.
(Faculty contact: Borsuk)

See also Linking predictive climate models to economic assessments under conditions of uncertainty