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

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

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

Аналіз текстури фрактографічних зображень на основі спектра фрактальних розмірностей Реньї

У статті досліджено застосування спектра фрактальних розмірностей Реньї для аналізу текстури фрактографічних зображень. Запропоновано використовувати цей підхід для класифікації типів зламів.В статье исследовано применение спектра фрактальных размерностей Реньи для анализа текстуры фрактографических изображений. Предложено использовать этот подход для классификации типов изломов.Investigated the use of the spectrum of fractal dimensions Renyi for texture analysis fraktohraphic images. Proposed to use this approach for the classification of types of fractures

Estimating the flood frequency distribution at seasonal and annual time scales

Abstract. We propose an original approach to infer the flood frequency distribution at seasonal and annual time scale. Our purpose is to estimate the peak flow that is expected for an assigned return period T, independently of the season in which it occurs (i.e. annual flood frequency regime), as well as in different selected sub-yearly periods (i.e. seasonal flood frequency regime). While a huge literature exists on annual flood frequency analysis, few studies have focused on the estimation of seasonal flood frequencies despite the relevance of the issue, for instance when scheduling along the months of the year the construction phases of river engineering works directly interacting with the active river bed, like for instance dams. An approximate method for joint frequency analysis is presented here that guarantees consistency between fitted annual and seasonal distributions, i.e. the annual cumulative distribution is the product of the seasonal cumulative distribution functions, under the assumption of independence among floods in different seasons. In our method the parameters of the seasonal frequency distributions are fitted by maximising an objective function that accounts for the likelihoods of both seasonal and annual peaks. In contrast to previous studies, our procedure is conceived to allow the users to introduce subjective weights to the components of the objective function in order to emphasize the fitting of specific seasons or of the annual peak flow distribution. An application to the time series of the Blue Nile daily flows at the Sudan–Ethiopia border is presented

Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach

During the last years, machine learning-based control and optimization systems are playing an important role in the operation of wastewater treatment plants in terms of reduced operational costs and improved effluent quality. In this paper, a machine learning-based control strategy is proposed for optimizing both the consumption and the number of regulation violations of a biological wastewater treatment plant. The methodology proposed in this study uses neural networks as a soft-sensor for on-line prediction of the effluent quality and as an identification model of the plant dynamics, all under a neuro-genetic optimum model-based control approach. The complete scheme was tested on a simulation model of the activated sludge process of a large-scale municipal wastewater treatment plant running under the GPS-X simulation frame and validated with operational gathered data, showing optimal control performance by minimizing operational costs while satisfying the effluent requirements, thus reducing the investment in expensive sensor devices.info:eu-repo/semantics/publishedVersio