Data-Based Nonlinear Model Identification in Economic Model Predictive Control

Many chemical/petrochemical processes in industry are not completely modeled from a first-principles perspective because of the complexity of the underlying physico-chemical phenomena and the cost of obtaining more accurate, physically relevant models. System identification methods have been utilized successfully for developing empirical, though not necessarily physical, models for advanced model-based control designs such as model predictive control (MPC) for decades. However, a fairly recent development in MPC is economic model predictive control (EMPC), which is an MPC formulated with an economics-based objective function that may operate a process in a dynamic (i.e., off steady-state) fashion, in which case the details of the process model become important for obtaining sufficiently accurate state predictions away from the steady-state, and the physics and chemistry of the process become important for developing meaningful profit-based objective functions and safety-critical constraints. Therefore, methods must be developed for obtaining physically relevant models from data for EMPC design. While the literature regarding developing models from data has rapidly expanded in recent years, many new techniques require a model structure to be assumed a priori, to which the data is then fit. However, from the perspective of developing a physically meaningful model for a chemical process, it is often not obvious what structure to assume for the model, especially considering the often complex nonlinearities characteristic of chemical processes (e.g., in reaction rate laws). In this work, we suggest that the controller itself may facilitate the identification of physically relevant models online from process operating data by forcing the process state to nonroutine operating conditions for short periods of time to obtain data that can aid in selecting model structures believed to have physical significance for the process and, subsequently, identifying their parameters. Specifically, we develop EMPC designs for which the objective function and constraints can be changed for short periods of time to obtain data to aid in model structure selection. For one of the developed designs, we incorporate Lyapunov-based stability constraints that allow closed-loop stability and recursive feasibility to be proven even as the online “experiments” are performed. This new design is applied to a chemical process example to demonstrate its potential to facilitate physics-based model identification without loss of closed-loop stability. This work therefore reverses a question that has been of interest to the control community (i.e., how new techniques for developing models from data can be useful for control of chemical processes) to ask how control may be utilized to impact the use of these techniques for the identification of physically relevant process dynamic models that can aid in improving process operation and control for economic and safety purposes

Economic Model Predictive Control Design via Nonlinear Model Identification

Increasing pushes toward next-generation/smart manufacturing motivate the development of economic model predictive control (EMPC) designs which can be practically deployed. For EMPC, the constraints, objective function, and accuracy of the state predictions would benefit from process models that describe the process physics. However, obtaining first- principles models of chemical process systems can be time-consuming or challenging such that it is preferable to develop physics-based process models automatically from process operating data. In this work, we take initial steps in this direction by suggesting that because experiments that are used to characterize first-principles models often target specific types of data, an EMPC may be utilized to gather non-routine operating data that ideally provides insights on the process physics and thereby allows physics-based process models to be developed on-line. These models can then be used to update the model, objective function, and constraints of the controller. Closed-loop stability and recursive feasibility considerations are discussed for the proposed EMPC design, and the controller\u27s application is illustrated through a chemical process example

Economic Model Predictive Control Design via Nonlinear Model Identification

Increasing pushes toward next-generation/smart manufacturing motivate the development of economic model predictive control (EMPC) designs which can be practically deployed. For EMPC, the constraints, objective function, and accuracy of the state predictions would benefit from process models that describe the process physics. However, obtaining first- principles models of chemical process systems can be time-consuming or challenging such that it is preferable to develop physics-based process models automatically from process operating data. In this work, we take initial steps in this direction by suggesting that because experiments that are used to characterize first-principles models often target specific types of data, an EMPC may be utilized to gather non-routine operating data that ideally provides insights on the process physics and thereby allows physics-based process models to be developed on-line. These models can then be used to update the model, objective function, and constraints of the controller. Closed-loop stability and recursive feasibility considerations are discussed for the proposed EMPC design, and the controller\u27s application is illustrated through a chemical process example

Organogels for low-polar organic solvents. Potential applications on cultural heritage materials

Traditional cleaning methods for removing undesired substances from artworks often involve the use of toxic volatile solvents, raising concerns about human health and environmental impact. Over time, various cleaning systems, such as thickeners, rigid, peelable, and nanostructured gels, have been introduced in the conservation sector to minimize solvent use and toxicity. However, these methods are primarily tailored for aqueous solutions or medium-to-high-polar solvents, leaving sustainable organogels for low-polar solvents largely unexplored. This paper explores the application of Low-Molecular-Weight Gelators (LMWGs) in the field of cultural heritage conservation, focusing on their potential to gel low-polar organic solvents. LMWGs, including cholesterol derivatives, fatty acidderived compounds, anthryl, anthraquinone, amino acid, and saccharide-based organogelators, offer biocompatible and cost-effective options by forming supramolecular gels that immobilize solvents and reduce their release into the environment. This study highlights the need to transition from traditional, often toxic, solvents to greener and more sustainable cleaning systems by emphasizing LMWGs’ biodegradability, biocompatibility, and sustainability. While challenges such as optimizing gel properties and ensuring compatibility with artwork surfaces still need to be addressed, LMWGs hold promise as organogelators in conservation practice. Further research into LMWGs should focus on their optimization for conservation applications by adjusting their rheological properties and physico-chemical stability

Indocyanine Green-Based Fluorescence-Guided Surgery in a Male Infant with Anorectal Malformation

Reconstructive techniques for complex anorectal malformations (ARMs) require intestinal pull-through on vascular pedicles. Traditionally, the visual inspection of the intestinal perfusion is the sole modality adopted to assess tissue viability. In this article, we report the case of a child with a rectourethral prostatic fistula, who had a Peña's descending colostomy with distal mucous fistula in the neonatal period and a posterior sagittal anorectoplasty at 6 months of life. The ARM repair was guided by indocyanine green (ICG), which was intravenously administered to evaluate the blood flow of the intestinal pull-through using the EleVision IR system (Medtronic Ltd, U.K.). ICG-based fluorescence-guided surgery helped to define the proximal resection margin, impacting intraoperative decision making, and no postoperative complications occurred. We envisage that this technology will become part of the armory of pediatric surgeons soon, by reducing the risk of intra- and postoperative complications

Do Non-Aromatic Labiatae Produce Essential Oil? The Case Study of Prasium Majus L

Prasium majus L. (Labiatae, Lamioideae) is considered a typical non-aromatic plant. In this work we examined the glandular trichomes present on leaves and inflorescences and the essential oils of plants growing along the Tuscan coast of Italy. The micromorphological study evidenced different types of trichomes responsible for the essential oil production. The essential oil compositions of leaves and flowers were analyzed by GC/MS and are here reported

Pharmacological effect of one icv dose of Allopregnanolone in female rat: behavioural profile

We have previously observed that intracerebroventricular allopregnanolone (ALLO) injection produced an anxiolytic effect and inhibited sexual receptivity when the test was performed in a separate manner. Also, ALLO reverts learning deficit in female rats in the hippocampi. To study the behavioral effects of an acute treatment with ALLO in the right lateral ventricle we used two approaches: a- A battery test to analyze the anxiety and mating behavior. And b- The avoidance test and novel object recognition test to evaluate its effect on memory and learning. Ovariectomized rats were injected with estrogen and progesterone. After it ALLO or vehicle were administered into the right lateral ventricle. To reach the objective (a) rats were put in a sequential battery test in the next order: 1-Open field. 2- Plus maze task. 3- Mating behavior. For the aim (b) it was performed a Novel Object Recognition Test and Step-down Inhibitory Avoidance Task. ALLO did not affect locomotors-exploratory behavior. Animals treated with ALLO, spent more time and had more entries into the open arm in a plus maze task and lordosis quotient was lower than in the control group. ALLO increased the latency in step down test and had no effects on discrimination index test in NORT. Here we demonstrated that one pharmacological dose of ALLO in ovariectomized primed rats is enough to generate all changes observed in the battery test. Moreover, the acute treatment with ALLO in lateral ventricle enhanced the memory acquisition in an avoidance task.Fil: Pelegrina, Laura Tatiana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Escudero, Carla Gimena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Giuliani, Fernando Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: García Menéndez, Sebastián Marcelo Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Cabrera Kreiker, Ricardo Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Laconi, Myriam Raquel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentin

Herramientas de gestión: relación entre concentraciones de material particulado fracción gruesa (MP10) y material particulado fracción fina (MP2.5) en calidad del aire

Los sistemas más habituales de monitoreo de la calidad del aire proporcionan datos basados en la medición del MP10 respecto a otros tamaños del material particulado. Como consecuencia, la mayoría de los estudios epidemiológicos utilizan el valor de concentración de la fracción MP10 como indicador de la exposición de la población. Sin embargo, numerosos trabajos científicos dan evidencia de mayores efectos sobre la salud asociados al MP2.5 en comparación con el anterior. En tal sentido contar con mediciones de esta fracción fina (MP2.5) en calidad de aire resulta de suma relevancia. En la práctica, no es sencillo acceder a esta información; principalmente por los altos costos que implican estas determinaciones. Por ende, se destaca la importancia de contar con un factor que relacione ambos parámetros, tal como lo ha estimado la Organización Mundial de la Salud (OMS), obteniendo un valor aproximado a 0.5 (MP2.5/MP10). Este número corresponde a zonas urbanas de los países en desarrollo. En el presente trabajo se estiman factores locales, en base a mediciones de MP10 y MP2.5, con el propósito de caracterizar las zonas urbanas evaluadas, acceder a un diagnóstico más preciso de distribución de la fracción fina y constituir así una herramienta de gestión

Detection of GAD65 Autoreactive T-Cells by HLA Class I Tetramers in Type 1 Diabetic Patients

Type 1 diabetes (T1D) is an autoimmune disease, in which pancreatic β cells are destroyed in genetically predisposed individuals. While the direct contribution of autoantibodies to the disease pathogenesis is controversial, it is generally recognised that the mechanism of β cell destruction is mediated by autoreactive T cells that had escaped the thymic selection. We aimed to design a method to detect circulating CD8+ T cells autoreactive against an epitope of the glutamic acid decarboxylase autoantigen, isoform 65 (GAD65) ex vivo in T1D patients by using HLA class I tetramers. Low frequencies of GAD65 peptide-specific CD8+ cytotoxic T lymphocytes were detected in peripheral blood lymphocytes (PBMC) of normal controls after GAD65 peptide-specific stimulation. Conversely, their frequencies were significantly higher than in controls in PBMC of T1D patients after GAD65 peptide stimulation. These preliminary data are encouraging in order to develop a reliable assay to be employed in large-scale screening studies

Image-guided surgery and novel intraoperative devices for enhanced visualisation in general and paediatric surgery: a review

Fluorescence guided surgery, augmented reality, and intra-operative imaging devices are rapidly pervading the field of surgical interventions, equipping the surgeon with powerful tools capable of enhancing the surgical visualisation of anatomical normal and pathological structures. There is a wide range of possibilities in the adult population to use these novel technologies and devices in the guidance for surgical procedures and minimally invasive surgeries. Their applications and their use have also been increasingly growing in the field of paediatric surgery, where the detailed visualisation of small anatomical structures could reduce procedure time, minimising surgical complications and ultimately improve the outcome of surgery. This review aims to illustrate the mechanisms underlying these innovations and their main applications in the clinical setting