(6kf) Uncertainty Quantification and Risk-based Decision Support Methodologies for Healthcare and Advanced Manufacturing | AIChE

(6kf) Uncertainty Quantification and Risk-based Decision Support Methodologies for Healthcare and Advanced Manufacturing


Rossi, F. - Presenter, Purdue University
Manenti, F., Politecnico di Milano
Buzzi-Ferraris, G., Politecnico di Milano
Reklaitis, G. V. R., Purdue University
Research interests:

In the last decade, there has been a rapid growth in the application of optimization techniques to the solution of problems of industrial importance. In particular, the process and related industries have recently expanded the systematic use of optimization in supply-chain management, operational planning and scheduling, and optimal process control. Even pharmaceutical companies, which have been conservative in model-based decision support innovations, have turned to optimization for product design and drug delivery purposes. This global trend, primarily motivated by increasing competition, calls for further developments in optimization and alike research fields, e.g. dynamic and mixed-integer optimization, model uncertainty quantification, optimization under uncertainty and risk assessment.

In view of these considerations, my passion for applied mathematics and statistics, and my rigorous chemical engineering education, my research expertise and current research interests span the following topics:

  • Dynamic modeling of chemical and pharmaceutical processes;
  • Quantification of parametric uncertainty in nonlinear models;
  • Risk assessment and risk-based decision making in process operations;
  • Deterministic and stochastic dynamic optimization;
  • Operational planning and scheduling of batch processes;
  • Supply-chain and enterprise-wide optimization;

Specific examples of my research achievements include a framework for simultaneous optimization of the supply-chain and production systems of industrial gas producers [1], two new strategies for deterministic and stochastic dynamic optimization of batch operations ([2,3] and [4,5]), and a novel approach to the integration of scheduling, dynamic optimization and control in multi-unit batch processes [6]. These strategies are very flexible, and will accommodate virtually any batch processing system (reactors and bio-reactors, crystallizers, freeze-drying units, home appliances, ...) and performance metric (economic functions, indicators of process environmental impact, ...). Thus, they constitute a general, robust optimization platform for batch processes.

Additional research work, completed in the last two years, comprises an innovative method for rapid estimation of the probability distribution (PDF) of the parameters of ODE/DAE models [7,8] and a strategy for dynamic selection of the most appropriate uncertainty set in stochastic dynamic optimization problems [9], which together allow real-time optimization of systems with stochastic and time-varying features (reactors/crystallizers subject to fouling, food processing units, ...). These two approaches seek to exploit the rapid growth in the availability of process data, which makes it possible to model the underlying uncertainties that arise in real-life process applications. Therefore, they constitute the backbone of a structured approach to the analysis and utilization of big data sets.

My current research efforts are devoted to developing new optimization-driven Monte Carlo algorithms for PDF estimation [7,10] and to the application of dynamic modelling, uncertainty quantification, and deterministic/stochastic optimization to processes for purification of monoclonal antibodies. The development of the aforementioned new types of Monte Carlo algorithms is justified by their superior reliability and computational efficiency, which are essential for uncertainty quantification in complex dynamic models, such as those of most processes for purification of monoclonal antibodies.

In view of all of my research achievements, my future research plan includes the following activities:

  • Development of a general platform for optimal drug delivery applicable to both standard drugs (e.g. insulin, pediatric drugs, opioids, biologics) and/or veterinary drugs. One of the several applications of this platform may be an improved artificial pancreas featuring fully automated model tuning, more reliable insulin administration policies and automatic compensation for long-term variations in the patient’s response to treatment. Other potential applications encompass pediatric drug delivery with automatic minimization of the risk for side effects, optimal administration of opioids to mitigate drug abuse and addiction problems, and ideal administration of conventional and/or broad spectrum antibiotics to animals (aminoglycosides, macrolides, penicillins, tetracyclines, ...).
  • Development of general methodologies for the rational design of formulations of biologics with applications to monoclonal antibodies and other biologic drugs of interest. These same approaches may as well be applicable to conventional drugs.
  • Definition of a general framework for rational design and online optimal management of pharmaceutical processes comprised of components for optimal process synthesis, dynamic optimization of upstream operations and risk-based real-time decision support for downstream processing steps. Some divisions of the pharmaceutical industry, which this framework can benefit, include generic drug manufacturing, production of biologics and/or of drugs subject to rapid degradation, and manufacturing of drugs/drug products from materials/intermediates difficult to process.
  • Definition of a general framework for rational design and dynamic optimization of food processing plants with potential applications to the production of nutrition supplements (e.g. artificial meat), the fermentation of wine and liquors, the spray drying of milk and natural extracts, the lyophilization of fruits and vegetables, and so on.
  • Development of accurate PDF estimation strategies applicable to high-dimensional spaces.
  • Development of stochastic dynamic optimization algorithms able to account for both parametric and structural model uncertainty as well as other nonconventional sources of uncertainty (e.g. the initial conditions of the problem).
  • Development of quantitative risk assessment strategies under both parametric and structural model uncertainty.

All of these activities will require use and extension of the methods and tools, described previously, as well as development of completely new ones. This tentative research plan may be partially modified based on funding availability and potential collaborations with other faculty members.

Teaching interests:

My research work and graduate studies have provided me with deep knowledge of control theory, numerical methods, dynamic and steady-state modeling, coding/parallel computing (especially in C++), and statistics. Therefore, I would be interested in teaching any course broadly related to these subjects, e.g. applied statistics, process control, numerical calculus, dynamic and steady-state modelling, and process optimization. I would also like to introduce and teach two advanced courses, whose topics lie at the boundaries of Chemical Engineering, Statistics and Computer Science:

  • Applied statistics and big data analytics for chemical engineers;
  • Applications of parallel computing to chemical engineering problems.

Since optimization strategies have become an essential tool for the solution of many problems of industrial relevance in the last few years, I strongly believe these interdisciplinary courses would provide new chemical engineers with useful and valuable knowledge.


  1. Rossi F, Manenti F, Reklaitis G. A general modular framework for the integrated optimal management of an industrial gases supply-chain and its production systems. Computers and Chemical Engineering. 2015;82:84-104.
  2. Rossi F, Manenti F, Buzzi-Ferraris G. A novel all-in-one real-time optimization and optimal control method for batch systems: Algorithm description, implementation issues, and comparison with the existing methodologies. Industrial and Engineering Chemistry Research. 2014;53:15639-15655.
  3. Rossi F, Copelli S, Colombo A, Pirola C, Manenti F. Online model-based optimization and control for the combined optimal operation and runaway prediction and prevention in (fed-) batch systems. Chemical Engineering Science. 2015;138:760-771.
  4. Rossi F, Reklaitis G, Manenti F, Buzzi-Ferraris G. Multi-scenario robust online optimization and control of fed-batch systems via dynamic model-based scenario selection. AIChE Journal. 2016;62:3264-3284.
  5. Rossi F, Manenti F, Pirola C, Mujtaba I. A robust sustainable optimization & control strategy (RSOCS) for (fed-)batch processes towards the low-cost reduction of utilities consumption. Journal of Cleaner Production. 2016;111:181-192.
  6. Rossi F, Casas-Orozco D, Reklaitis G, Manenti F, Buzzi-Ferraris G. A computational framework for integrating campaign scheduling, dynamic optimization and optimal control in multi-unit batch processes. Computers and Chemical Engineering. 2017;107:184-220.
  7. Rossi F, Mockus L, Reklaitis G. Rigorous Bayesian inference VS new approximate strategies for estimation of the probability distribution of the parameters of DAE models. Computer Aided Chemical Engineering. Accepted (oral presentation at ESCAPE29, Eindhoven, NL).
  8. Rossi F, Manenti F, Buzzi-Ferraris G, Reklaitis G. A novel strategy for rapid estimation of the probability distribution of the uncertain parameters of ODE/DAE systems. In preparation. Estimated submission at the end of 2019.
  9. Rossi F, Manenti F, Buzzi-Ferraris G, Reklaitis G. Stochastic NMPC/DRTO of batch operations: batch-to-batch dynamic identification of the optimal description of model uncertainty. Computers and Chemical Engineering. 2019;122:395-414.
  10. Rossi F, Mockus L, Reklaitis G. A novel quasi-deterministic Monte Carlo method for estimation of probability distributions. In preparation. Estimated submission at the end of 2019.