(6ic) Novel Strategies for Real-Time Stochastic Optimization, Quantification of Model Uncertainty and Estimation of the Physical Properties of Biologics

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:

• Supply-chain and enterprise-wide optimization applied to continuous processes;
• Operational planning and scheduling of batch processes;
• Deterministic and stochastic online optimization of batch processes;
• Quantification of parametric uncertainty in nonlinear models;
• Identification of optimal uncertainty sets in stochastic dynamic optimization problems;
• Prediction of the physical properties of formulations of biologics;

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 single batch units ([2,3] and [4,5]), and a novel approach to the integration of scheduling, dynamic optimization and control in multi-unit batch processes [6]. The algorithms, developed in [2,3] and [6], assume that the process model is accurate while the approach, proposed in [4,5], explicitly accounts for model uncertainty, through a novel dynamic scenario selection procedure. These three 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 year, comprises an innovative method for rapid estimation of the probability distribution function (PDF) of the uncertain parameters of ODE/DAE models [7,8] and a strategy for dynamic selection of the best uncertainty set in stochastic dynamic optimization problems [9]. The first method relies on uncertainty back-propagation and projection techniques, and offers a very good trade-off between accuracy and computational cost. The second is based on a combination of fast PDF estimation, multi-point sensitivity analysis and ranking strategies, and allows application of stochastic dynamic optimization to 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 a general strategy for prediction of the thermodynamic and transport properties of mixtures of monoclonal antibodies (mABs), excipients and water. This approach, based on the SAFT equation of state, is an essential component of a more complex framework, aimed at identifying the most promising mAB formulations without expensive and time-consuming experimental analyses.

My research plan for the near future includes several activities, which can be summarized as follows:

• Application of stochastic optimization to drug delivery problems (administration of mABs, chelation therapy, pediatric drug administration, …);
• Application of stochastic optimization to critical food processing operations (freeze-drying, addition of preservatives, pasteurization, …);
• Application of stochastic optimization to the optimal design of mAB formulations;
• Development of stochastic dynamic optimization strategies able to account for both parametric and structural model uncertainty;
• Development of frameworks for multi-objective, robust, integrated scheduling, dynamic optimization and control of batch processes;
• Development of robust soft sensors for real-time risk assessment in pharmaceutical systems.

All of these activities will require use and extension of the frameworks, described previously, as well as development of completely new ones. This tentative research plan will of course be 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 modelling, 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 methods, 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.

References:

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, Manenti F, Reklaitis G. Assessment of accuracy and computational efficiency of different strategies for estimation of probability distributions applied to ODE/DAE systems. Computer Aided Chemical Engineering. Accepted.
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 2018.
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. Under review.