The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation

Robust control of pH in a raceway photobiorreactor

[EN] This work presents a first approach to the robust control of pH in a raceway photobioreactor for the cultivation of microalgae. In this type of process, pH is the most critical variable to control, having a direct relationship with the productivity of the system. The dynamics of the pH has a strongly non-linear character, being affected by numerous factors such as the contribution of CO2 to the culture medium or the performance of photosynthesis by the microalgae. This non-linearity generates a great source of uncertainty in the process even when the system is controlled around the desired operating point. Therefore, in this article we have proceeded to model the system with parametric uncertainty covering the typical working ranges of pH, and later a robust controller with QFT is designed to achieve certain robust performance and stability requirements. The resulting control algorithm has been evaluated in simulation and in real tests against different working conditions and at different operating points, obtaining satisfactory results. [ES] Este  trabajo  presenta  una  primera  aproximación al control robusto del pH en un fotobiorreactor raceway  para  el  cultivo  de  microalgas. En este tipo de procesos el pH es la variable más crítica a controlar teniendo una relación directa con la productividad del sistema. La dinámica del pH posee un carácter fuertemente no lineal estando afectada por numerosos factores tales como el aporte del CO2 al medio de cultivo o la realización de la fotosíntesis por parte de las microalgas. Esta no linealidad genera una gran fuente de incertidumbre en el proceso incluso cuando el sistema es controlado alrededor del punto de operación deseado. Por tanto, en este artículo se ha procedido a realizar el modelado del sistema con incertidumbre paramétrica cubriendo los rangos de trabajo típicos del pH, y posteriormente se ha realizado el diseño de un controlador robusto con la técnica Quantitative Feedback Theory (QFT) para conseguir unos requisitos de rendimiento y estabilidad robustos determinados. El algoritmo de control resultante se ha evaluado en simulación y mediante ensayos reales frente a distintas condiciones de trabajo y en distintos puntos de operación, obteniéndose resultados satisfactorios.Esta publicacion es parte de los proyectos de I +D+i PID2020-112709RB-C21 y PID2020-112709RB-C22, financiado por el Ministerio de Ciencia e Innovación y FEDER “Una manera de hacer Europa”.Hoyo Sánchez, Á.; Guzmán Sánchez, JL.; Moreno Úbeda, JC.; Baños Torrico, A. (2022). Control robusto del pH en un fotobiorreactor raceway. Revista Iberoamericana de Automática e Informática industrial. 19(3):274-283. https://doi.org/10.4995/riai.2022.16731OJS27428319

Estimation of microalgae production in industrial photobioreactors

[Resumen] Los reactores de microalgas proporcionan una alternativa eficiente y limpia para la producción de biogas, biofuel, productos nutricionales y cosm´eticos, etc. El principal objetivo de control en estos sistemas es la optimización de la productividad, por lo que resulta crucial la monitorización de la concentración de biomasa en el reactor para determinarla en tiempo real. Pese a ello, no existen en el mercado soluciones suficientemente robustas, en especial para los reactores abiertos a escala industrial. En este trabajo se presentan unos primeros resultados en el desarrollo de un nuevo estimador de biomasa en línea, basado en un observador muy robusto, el de modos deslizantes, combinado con un modelo dinámico no lineal y variante en el tiempo dotado de un número mínimo de estados, que permiten capturar los aspectos esenciales del proceso de producción de microalgas. El observador se ha testado con un modelo completo del reactor y las simulaciones muestran resultados prometedores en términos de precisión y robustez.[Abstract] Microalgae reactors provide an efficient and clean alternative for production of biogas, biofuel, nutritional and cosmetic products, etc. The main control objective in these systems is the optimization of productivity. For this reason, it is crucial monitoring the biomass concentration in the reactor and so to determine the productivity in real time. Despite this, there are no sufficiently robust solutions on the market, especially for open reactors on an industrial scale. This paper presents the first results in the development of a new online biomass estimator, based on a very robust observer, the sliding modes observer , combined with a nonlinear and time-varying dynamic model endowed with a minimum number of states, which allow capturing the essential aspects of the microalgae production process. This soft-sensor has been tested with a complete model of the reactor and the simulations show promising results in terms of accuracy and robustness.Ministerio de Ciencia e Innovación; PID2020-112709RB-C2

Modelado y predicción a corto plazo del consumo y producción de energía eléctrica en una micro-red utilizando métodos basados en series temporales y redes neuronales artificiales

[Resumen] La previsión de la demanda eléctrica juega un papel clave en el funcionamiento de los sistemas de energía, esto se debe a que la generación de energía por medio de fuentes renovables y sistemas distribuidos está creciendo en muchos países. Un análisis y modelado de la potencia eléctrica de los sistemas que conforman una Micro-red (MG) se presenta en este trabajo. El sistema que se estudia consiste en una MG, esta MG se encuentra integrada por un sistema fotovoltaico (PV), un edificio bioclimático, un invernadero, un vehículo eléctrico y una interconexión con la red eléctrica que permite la compra y venta de energía. Este trabajo se centra en el estudio de cómo los métodos basados en series temporales y redes neuronales se pueden utilizar para las predicciones de la demanda de energía. El principal objetivo de los métodos presentados en este documento es predecir el comportamiento futuro a corto plazo. Se proporcionan comparaciones entre los diferentes métodos de predicción de energía.Este trabajo ha sido financiado con el Proyecto R+D+i del Plan Nacional DPI2014-56364-C2-1-R del Ministerio de Economía y Competitividad y Fondos FEDERhttps://doi.org/10.17979/spudc.978849749808

Un enfoque óptimo para la distribución de energía de una micro-red usando Control Predictivo basado en Modelo (MPC): una simulación de un caso de estudio

[Resumen] Las micro-redes permiten la integración de fuentes de energía renovables como fuentes de energía solar y eólica, también permiten la integración de sistemas distribuidos tales como producción combinada de calor y energía y almacenamiento de energía. Además, el uso de las fuentes locales de energía para servir cargas locales ayuda a reducir las pérdidas de energía en la transmisión y distribución, aumentando aún más la eficiencia del sistema de suministro eléctrico. En este trabajo, el problema de optimización de la energía dentro de una micro-red de energía renovable (MG) con un sistema de almacenamiento de energía (ESS), que intercambia energía con la red principal se desarrolla y se resuelve con el uso de técnicas de control predictivo basado en modelo (MPC). El modelado del sistema utiliza la metodología de los Energy Hubs. Las técnicas de MPC permiten maximizar el beneficio económico de la micro-red y reducir al mínimo la degradación del sistema de almacenamiento.Este trabajo ha sido financiado con el Proyecto R+D+i del Plan Nacional DPI2014-56364-C2-1-R del Ministerio de Economía y Competitividad y Fondos FEDERhttps://doi.org/10.17979/spudc.978849749808

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort

Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients

Background: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. Methods: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. Results: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). Conclusions: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

Key Factors Associated With Pulmonary Sequelae in the Follow-Up of Critically Ill COVID-19 Patients

Introduction: Critical COVID-19 survivors have a high risk of respiratory sequelae. Therefore, we aimed to identify key factors associated with altered lung function and CT scan abnormalities at a follow-up visit in a cohort of critical COVID-19 survivors. Methods: Multicenter ambispective observational study in 52 Spanish intensive care units. Up to 1327 PCR-confirmed critical COVID-19 patients had sociodemographic, anthropometric, comorbidity and lifestyle characteristics collected at hospital admission; clinical and biological parameters throughout hospital stay; and, lung function and CT scan at a follow-up visit. Results: The median [p25–p75] time from discharge to follow-up was 3.57 [2.77–4.92] months. Median age was 60 [53–67] years, 27.8% women. The mean (SD) percentage of predicted diffusing lung capacity for carbon monoxide (DLCO) at follow-up was 72.02 (18.33)% predicted, with 66% of patients having DLCO < 80% and 24% having DLCO < 60%. CT scan showed persistent pulmonary infiltrates, fibrotic lesions, and emphysema in 33%, 25% and 6% of patients, respectively. Key variables associated with DLCO < 60% were chronic lung disease (CLD) (OR: 1.86 (1.18–2.92)), duration of invasive mechanical ventilation (IMV) (OR: 1.56 (1.37–1.77)), age (OR [per-1-SD] (95%CI): 1.39 (1.18–1.63)), urea (OR: 1.16 (0.97–1.39)) and estimated glomerular filtration rate at ICU admission (OR: 0.88 (0.73–1.06)). Bacterial pneumonia (1.62 (1.11–2.35)) and duration of ventilation (NIMV (1.23 (1.06–1.42), IMV (1.21 (1.01–1.45)) and prone positioning (1.17 (0.98–1.39)) were associated with fibrotic lesions. Conclusion: Age and CLD, reflecting patients’ baseline vulnerability, and markers of COVID-19 severity, such as duration of IMV and renal failure, were key factors associated with impaired DLCO and CT abnormalities