Virtual Sensor Based on a Deep Learning Approach for Estimating Efficiency in Chillers

P. 307-319Intensive use of heating, ventilation and air conditioning (HVAC) systems in buildings entails an analysis and monitoring of their e ciency. Cooling systems are key facilities in large buildings, and par- ticularly critical in hospitals, where chilled water production is needed as an auxiliary resource for a large number of devices. A chiller plant is often composed of several HVAC units running at the same time, be- ing impossible to assess the individual cooling production and e ciency, since a sensor is seldom installed due to the high cost. We propose a virtual sensor that provides an estimation of the cooling production, based on a deep learning architecture that features a 2D CNN (Convolu- tional Neural Network) to capture relevant features on two-way matrix arrangements of chiller data involving thermodynamic variables and the refrigeration circuits of the chiller unit. Our approach has been tested on an air-cooled chiller in the chiller plant at a hospital, and compared to other state-of-the-art methods using 10-fold cross-validation. Our re- sults report the lowest errors among the tested methods and include a comparison of the true and estimated cooling production and e ciency for a period of several daysS

Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System

Recent advancement in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. While accuracy is important, focusing primarily on it poses an overfitting danger, exposing manufacturers to risk, ultimately hindering the adoption of these techniques. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty in a cyber-physical manufacturing system (CPMS) scenario. Then, we propose a multi-agent system architecture which leverages probabilistic machine learning as a means of achieving such criteria. We propose possible scenarios for which our architecture is useful and discuss future work. Experimentally, we implement Bayesian Neural Networks for multi-tasks classification on a public dataset for the real-time condition monitoring of a hydraulic system and demonstrate the usefulness of the system by evaluating the probability of a prediction being accurate given its uncertainty. We deploy these models using our proposed agent-based framework and integrate web visualisation to demonstrate its real-time feasibility

Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation

Wind farm layout optimisation is a challenging real-world problem which requires the discovery of trade-off solutions considering a variety of conflicting criteria, such as minimisation of the land area usage and maximisation of energy production. However, due to the complexity of handling multiple objectives simultaneously, many approaches proposed in the literature often focus on the optimisation of a single objective when deciding the locations for a set of wind turbines spread across a given region. In this study, we tackle a multi-objective wind farm layout optimisation problem. Different from the previously proposed approaches, we are applying a high-level search method, known as selection hyper-heuristic to solve this problem. Selection hyper-heuristics mix and control a predefined set of low-level (meta)heuristics which operate on solutions. We test nine different selection hyper-heuristics including an online learning hyper-heuristic on a multi-objective wind farm layout optimisation problem. Our hyper-heuristic approaches manage three well-known multi-objective evolutionary algorithms as low-level metaheuristics. The empirical results indicate the success and potential of selection hyper-heuristics for solving this computationally difficult problem. We additionally explore other objectives in wind farm layout optimisation problems to gain a better understanding of the conflicting nature of those objectives

Zircon ages in granulite facies rocks: decoupling from geochemistry above 850 °C?

Granulite facies rocks frequently show a large spread in their zircon ages, the interpretation of which raises questions: Has the isotopic system been disturbed? By what process(es) and conditions did the alteration occur? Can the dates be regarded as real ages, reflecting several growth episodes? Furthermore, under some circumstances of (ultra-)high-temperature metamorphism, decoupling of zircon U–Pb dates from their trace element geochemistry has been reported. Understanding these processes is crucial to help interpret such dates in the context of the P–T history. Our study presents evidence for decoupling in zircon from the highest grade metapelites (> 850 °C) taken along a continuous high-temperature metamorphic field gradient in the Ivrea Zone (NW Italy). These rocks represent a well-characterised segment of Permian lower continental crust with a protracted high-temperature history. Cathodoluminescence images reveal that zircons in the mid-amphibolite facies preserve mainly detrital cores with narrow overgrowths. In the upper amphibolite and granulite facies, preserved detrital cores decrease and metamorphic zircon increases in quantity. Across all samples we document a sequence of four rim generations based on textures. U–Pb dates, Th/U ratios and Ti-in-zircon concentrations show an essentially continuous evolution with increasing metamorphic grade, except in the samples from the granulite facies, which display significant scatter in age and chemistry. We associate the observed decoupling of zircon systematics in high-grade non-metamict zircon with disturbance processes related to differences in behaviour of non-formula elements (i.e. Pb, Th, U, Ti) at high-temperature conditions, notably differences in compatibility within the crystal structure

class_sz I: Overview

class_sz is a versatile and robust code in C and Python that can compute theoretical predictions for a wide range of observables relevant to cross-survey science in the Stage IV era. The code is public at https://github.com/CLASS-SZ/class_sz along with a series of tutorial notebooks (https://github.com/CLASS-SZ/notebooks). It will be presented in full detail in paper II. Here we give a brief overview of key features and usage.Comment: to appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Science

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Activation of pancreatic stellate cells attenuates intracellular Ca2+ signals due to downregulation of TRPA1 and protects against cell death induced by alcohol metabolites

Alcohol abuse, an increasing problem in developed societies, is one of the leading causes of acute and chronic pancreatitis. Alcoholic pancreatitis is often associated with fibrosis mediated by activated pancreatic stellate cells (PSCs). Alcohol toxicity predominantly depends on its non-oxidative metabolites, fatty acid ethyl esters, generated from ethanol and fatty acids. Although the role of non-oxidative alcohol metabolites and dysregulated Ca2+ signalling in enzyme-storing pancreatic acinar cells is well established as the core mechanism of pancreatitis, signals in PSCs that trigger fibrogenesis are less clear. Here, we investigate real-time Ca2+ signalling, changes in mitochondrial potential and cell death induced by ethanol metabolites in quiescent vs TGF-β-activated PSCs, compare the expression of Ca2+ channels and pumps between the two phenotypes and the consequences these differences have on the pathogenesis of alcoholic pancreatitis. The extent of PSC activation in the pancreatitis of different aetiologies has been investigated in three animal models. Unlike biliary pancreatitis, alcohol-induced pancreatitis results in the activation of PSCs throughout the entire tissue. Ethanol and palmitoleic acid (POA) or palmitoleic acid ethyl ester (POAEE) act directly on quiescent PSCs, inducing cytosolic Ca2+ overload, disrupting mitochondrial functions, and inducing cell death. However, activated PSCs acquire remarkable resistance against ethanol metabolites via enhanced Ca2+-handling capacity, predominantly due to the downregulation of the TRPA1 channel. Inhibition or knockdown of TRPA1 reduces EtOH/POA-induced cytosolic Ca2+ overload and protects quiescent PSCs from cell death, similarly to the activated phenotype. Our results lead us to review current dogmas on alcoholic pancreatitis. While acinar cells and quiescent PSCs are prone to cell death caused by ethanol metabolites, activated PSCs can withstand noxious signals and, despite ongoing inflammation, deposit extracellular matrix components. Modulation of Ca2+ signals in PSCs by TRPA1 agonists/antagonists could become a strategy to shift the balance of tissue PSCs towards quiescent cells, thus limiting pancreatic fibrosis

A study of FMS part type selection approaches for short-term production planning

This research compares seven approaches from the literature to the selection of part types for simultaneous production over the next time horizon. A flexible approach to the selection of part types and the simultaneous determination of their mix ratios so as to balance aggregate machine workloads is presented. Constraints on tool magazine capacity are considered. Simulation studies are conducted on realistic, detailed models of flexible flow systems (FFSs) configured as pooled machines of equal sizes. The simulated settings are constructed to evaluate the impact of such factors as blocking, transportation, buffer utilizations, and fixture requirements and limitations of various types.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45438/1/10696_2004_Article_BF00713157.pd