Showing papers by "Gabriele Pannocchia published in 2022"

Data-driven Models for Advanced Control of Acid Gas Treatment in Waste-to-energy Plants

Abstract: This paper presents a study of identification and validation of data-driven models for the description of the acid gas treatment process, a key step of flue gas cleaning in waste-to-energy plants. The acid gas removal line of an Italian plant, based on the injection of hydrated lime, Ca(OH)2, for the abatement of hydrogen chloride, HCl, is investigated. The final goal is to minimize the feed rate of reactant needed to achieve the required HCl removal performance, also reducing as a consequence the production of solid process residues. Process data are collected during dedicated plant tests carried out by imposing Generalized Binary Noise (GBN) sequences to the flow rate of Ca(OH)2. Various input-output and state-space models are identified with success, and related model orders are optimized. The models are then validated on different datasets of routine plant operation. The proposed modeling approach appears reliable and promising for control purposes, once implemented into advanced model-based control structures.

Analytical RTO for a critical distillation process based on offline rigorous simulation

Abstract: The economic performance of distillation processes has been always addressed by techniques supporting and completing Model Predictive Control (MPC), which are known as Real Time Optimization (RTO). RTO, via simulation model, has been deeply studied and applied to distillation processes, nevertheless, the cost associated with the development of such online simulation models is often an obstacle for industries to change their operations. Hence, this work presents an unconventional RTO approach, named “analytical RTO”, which calculates the setpoint of manipulated variables according to operating strategy exploiting an analytical generalized formulation. The proposed RTO is built for a specific unit, that is a Propane-Propylene super-fractionator producing polymer grade Propylene (99.5%), and it is based on an offline simulation model developed in UniSim Design. Sensitivities analysis of the revenue with the manipulated variables is obtained with the simulation model and used to derive a simple, yet quite effective analytical RTO formulation, taking into account the fluidynamic limit of the unit studied. Comparing such an approach with the simulations, the results show a satisfactory match of the analytical estimations with the optimal points calculated by the rigorous model.

A continuous flow reactor for the flexible production of different formulations: CFD‐aided design

Abstract: The increasing need to improve the sustainability of industrial processes requires more flexible and intensified solutions. For this purpose, nowadays lots of efforts are made to switch from batch to continuous processes, the latter being able to ensure the same processing history to all fluid elements, with a consequent better control of the operating conditions and product quality. The present work aims at developing a continuous flow reactor for the production of several fine chemicals, including medical-surgical aids, but also other substances for specific industrial sectors. The plant is basically an inline reactor equipped with various static mixers and side inlets, and it is conceived to ensure on-site production. This is an important feature also in light of the recent COVID-19 pandemic, which asked for flexible and distributed production of chemicals. Numerical simulations based on computational fluid dynamics are employed to study the performance, in terms of pressure drops and degree of mixing, of different static mixers, that is, the Lightnin Inliner Series 50 and Ross low pressure drop (LPD), combining various elements of mixing and injections in different operating conditions in both laminar and turbulent regimes. The results highlighted how numerical simulations may represent a valid tool for supporting the detailed design of such flow reactors by allowing the evaluation of the optimal design solutions.