(8s) Deterministic and Robust Model-Based Strategies for the Online Multi-Level Optimization of Batch Operations

In the last few decades, there has been a steady growth in applying optimization techniques to the solution of problems of industrial importance, e.g. supply-chain management, operational planning/scheduling and optimal process control/dynamic optimization. This trend has been motivated by both increasing global competition and tighter environmental regulations, which are driving industry towards reducing costs as well as minimizing environmental impacts.

Although extensive process systems engineering research has been reported so far, most of it has addressed continuous processes and only limited attention has been devoted to batch operations, which are widely used to produce high added-value products (specialty chemicals, pharmaceuticals, cosmetics, etc.). Given the opportunities that this application domain provides, I have focused my research on methodologies/applications in operational planning/scheduling and optimal process control/dynamic optimization of batch processes. However, I have also addressed supply-chain problems¹ and continue having interests in such research area.

Most recently, I have focused on developing methods for the optimal online management of both single batch units and entire batch plants. I have formulated and implemented two dynamic optimization/optimal control algorithms applicable to single batch operations (BSMBO&C ^2,3, RBSMBO&C ^4,5) and a further one designed for (multi-unit) batch plants (MUBSMBO&C). These strategies are very flexible and can tackle the online optimization/optimal control of both single batch cycles and entire production campaigns. Moreover, RBSMBO&C also implements a novel dynamic scenario selection concept to handle model uncertainty.

My ongoing research involves studying uncertainty propagation/Bayesian inference with the goal of further improving RBSMBO&C. Moreover, I am also planning to extend MUBSMBO&C and make it capable of handling model uncertainty. Longer-term research perspectives include addressing the problem of supply-chain management of batch manufacturing systems and developing integrated frameworks for the simultaneous online optimization of multiple decisional levels.

Teaching Interests:

My research work and graduate studies have provided me with deep knowledge of control theory, numerical methods, dynamic/steady-state modelling and coding/parallel programming (especially in C++). Therefore, I would be interested in teaching any course (broadly) related to these subjects, e.g. process control/design, numerical methods, dynamic/steady-state modelling and process optimization.

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; DOI: 10.1002/aic.15346.
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.