On the closed-loop stochastic dynamics of two-state nonlinear exothermic CSTRs with PI temperature control

Fokker-Planck (FP) partial differential equation (PDE) theory is applied to characterize the stochastic dynamics of a class of open-loop (OL) 2-state nonlinear exothermic continuous reactors with: (i) zero and time-varying mean noise disturbances, and (ii) linear proportional-integral (PI) temperature control. The characterization includes: (i) the stochastic on deterministic dynamics dependency, (ii) gain condition for robust probability density function (PDF) stability over deterministic-diffusion time biscale with stationary monomodality at prescribed most probable (MP) state, (iii) evolutions of along nearly deterministic time scale of MP state and control and their variabilities, (iv) attainment of random motion in-probability (IP) stability over deterministic-diffusion time biscale, and (v) identification of the compromise between MP state regulation speed, robustness, and control effort. The methodological developments and findings are illustrated with three indicative examples with OL complex (bimodal and vulcanoid) stationary state PDFs, including analytic assessment as well as state PDF and random motion numerical simulation

Optimal strategies to control particle size and variance in antisolvent crystallization operations using deep RL

Solution crystallization operations have complex dynamics that are typically lumped into two competing processes namely nucleation and growth. Mathematical models can be used to describe these two processes and their effect on the crystal population when subject to variables like temperature, addition of anti-solvent, etc. To ensure that the crystals meet specific performance objectives, the models need to be solved and the control variables need to be optimized. This has largely been done until now using algorithms from dynamic programming or optimal control theory. Recently, however, it has been shown that learning frameworks like Reinforcement Learning can solve large optimization problems efficiently while offering distinct advantages. In this work, we explore the possibility of computing the optimal profiles of a semi-batch crystallizer to control the mean size and variance using four different deep RL algorithms. The performance on one of the tasks is evaluated experimentally on the anti-solvent crystallization of NaCl in a water-ethanol system

On the prediction of psd in antisolvent mediated crystallization processes based on fokker-planck equations

A phenomenological model for the description of antisolvent mediated crystal growth processes is presented. The crystal size growth dynamics is supposed to be driven by a deterministic growth factor coupled to a stochastic component. Two different models for the stochastic component are investigated: a Linear and a Geometric Brownian motion terms. The evolution in time of the particle size distribution is then described in terms of the Fokker-Planck equation. Validations against experimental data are presented for the NaCl-water-ethanol anti-solvent crystallization system. It was found that a proper modeling of the stochastic component does have an impact on the model capabilities to fit the experimental data. In particular, the GBM assumption is better suited to describe the antisolvent crystal growth process under examination

Solvent recovery system for a CO2-MEA reactive absorption-stripping plant

The solvent recovery section from the exhaust gas represents an important auxiliary part for an industrial CO2 post-combustion capture plant by the reactive absorption-stripping process. In this work, a partial condenser and a water-wash section configuration were designed to reach 1 ppm of solvent in the exhaust gas, and compared using the Total Annual Cost (TAC) as economic index. Both the configurations ensured the required recovery performance. The results highlighted that the partial condenser alternative is more convenient in terms of capital annualized costs and water make-up, but at the same time it is strongly penalized by the high operating costs for the cooling water. Therefore, the configuration in which the absorber is equipped with the water-wash section resulted the option with the minimum TAC

On the dynamics and robustness of the chemostat with multiplicative noise

The stochastic dynamics of a two-state bioreactor model with random feed flow fluctuations and non-monotonic specific growth rate is analyzed. Using the Fokker-Planck equation approach for describing the probability density function (PDF) evolution the lack of stochastic robustness due to deterministic bifurcation phenomena for the open-loop reactor operating under optimal (maximum production) operation condition is established, and the associated stochastic stabilization problem is addressed. Inherent differences between the presence of multiplicative noise, due to the feed flow fluctuations, and additive background noise are analytically established. Numerical simulation results illustrate these inherent differences, the stochastic fragility of the open-loop operation yielding a stochastic extinction phenomenon, as well as the stochastic PDF stabilization with a proportional feedback control

Machine learning for monitoring and control of NGL recovery plants

In this contribution, the monitoring and control problem of the natural gas liquids (NGL) extraction process is addressed by exploiting a data-driven approach. The cold residue reflux (CRR) process scheme is considered and simulated by using the process simulator Aspen HYSYS®, with the main targets of the achievement of 84% ethane recovery and low levels of methane impurity at the bottom of the demethanizer column. The respect of product quality is obtained by designing a proper control strategy that uses a data-driven approach based on a neural network to estimate the unmeasured outputs. The performance of the controlled system is assessed by simulating the process under various input conditions evaluating different control structures such as direct control and cascade control of the temperature in the column

Identification of a cell population model for algae growth processes

The growth process of a Chlamydomonas reinhardtii cell population is modelled with experimental data obtained in a batch reactor. To describe the growth process of this culture, the Droop model, extended by cell population balance model, is considered. On the basis of available measurements and the mathematical model, an optimization problem is defined in order to determine the kinetic parameter values for the growth functions of the Droop model and the cell division parameters of the cell population balance model

Control of a natural gas liquid recovery plant in a GSP unit under feed and composition disturbances

Recent technological improvements have driven the rapid increase in natural gas production from unconventional reservoirs. The heaviest hydrocarbon fraction of this fossil fuel, the so-called natural gas liquids (NGL), have greater economic interest justifying the attention on its separation process from the raw gas. Various process schemes have been developed and studied for the NGL recovery, including the conventional, cold residue recycle (CRR), and the gas subcooled process (GSP). This study aims to assess different control strategies for a GSP unit and determine the most appropriate and effective process control scheme. For this, the dynamic responses for each control scheme are evaluated by changing feed flow rate and composition. The main targets are the achievement of 84% ethane recovery and low levels of methane impurity at the bottom of the demethanizer column. Due to the high cost of composition analyzers and the high delays introduced by composition controllers under the presence of flow disturbances, the control goals are reached by the knowledge of on-line temperature measurements. This is done by considering different temperature control structures such as the direct temperature control and cascade control, plus a pressure compensator. The results are compared, in presence of composition disturbances, with the action of a hybrid cascade control that uses in-line delayed concentration measurements to update the controller reference at each sampling period. Here, the hybrid and the simple cascade controls show the best control performance

Argas (Persicargas) persicus (Oken, 1818) (Ixodida: Argasidae) in Sicily with considerations about its Italian and West-Mediterranean distribution.

Recently, in the province of Trapani (Western Sicily), some overwintering specimens of the argasid tick Argas (Persicargas) persicus (Oken, 1818) were observed and collected. Morphological and genetic analysis were utilized in order to reach a definitive identification. The species was found in two semi-natural sites where, having been found repeatedly, its presence does not appear accidental. Moreover the characteristics of the Sicilian findings seem to exclude a human-induced spread. This record, the first regarding Sicily and South Italy, is discussed together with the previous doubtful citations for Italy. These findings revalue not only all the old citations for Italy but also the hypothesis that the Mediterranean distribution of this argasid is of a natural origin