Multivariate KPI for energy management of cooling system in food industry

Within EU, the food industry is currently ranked among the energy-intensive sectors, mainly as a consequence of the cooling system shareover the total energy demand. As such, the definition of appropriate key performance indicators (KPI) for ammonia chillers can play a strategic role for the efficient monitoring of the energy performance of the cooling systems. The goal of this paper is to develop an appropriate management approach, to account for energy inefficiency of the single compressors, and to identify the specific variables driving the performance outliers. To this end, a new KPI is proposed which correlates the energy consumption and the different process variables. The construction of the new indicator was carried out by means of multivariate statistical analysis, in particular using Kernel Partial Least Square (KPLS).This method is able to evaluate the maximum correlation between dataset and energy consumption employing nonlinear regression techniques. The validity of the new KPI is discussed on a case study relevant to the cooling system of a frozen ready meals industry. The assessment of the proposed metric is one against Specific Energy Consumption (SEC) like indicator, typically used in the context of the Energy Management Systems

Industrial energy management systems in Italy: state of the art and perspective

Despite the economic crisis, the impact of industry sector Share on the total primary energy demand in Italy is still significant. The certification of companies according to the standard ISO 50001:2011 ("Energy management systems Requirements and guidelines for use"), can represent a key element in the achievement of objectives set in the 20-20-20 Climate-Energy Package. This paper illustrates the state of implementation of ISO 50001 certifications in Italy, reporting on the results of a questionnaire carried out as a part of a master's thesis project at Sapienza, University of Rome in collaboration with FIRE (Italian Federation for the Rational Use of Energy) that included the major certification bodies, certified companies and consultants. The purpose is to outline the current situation, identify the perspectives and highlight the pros and cons related to the implementation of an Energy Management System (EnMS). The big picture shows that Italy, one of the leading countries in energy efficiency policies, suffer from a significant delay in the implementation of the EnMS in industry with respect to Germany. The results of the survey also show that the definition of energy performance indicators, as hell as the individuations of an energy baseline and a. monitoring plan constitute the requirements most critical to comply with for companies than for consultants. It also appears that more than 35% of companies already ISO 50001 certified have received benefits in terms of cumulative energy saving above 5%, and that the main reason why they have implemented an EnMS is related to the potential impact on increasing the competitiveness of the core business

Multivariate Key Performance Indicator of Baking Process

Abstract Energy efficiency is nowadays a subject deeply discussed in several fields, with a large potential in industry. Here proper process and energy management routines turns out to be essential to reduce the energy demand while keeping the control of the product quality. In this respect the bread baking, one of the pillar of food related industry, is an energy intensive process irrespective to the adopted oven technology or to the primary energy nature. Baking is the fundamental step of the bread production process and it entails a number of complex chemical and physical phenomena, critical to the final physical properties of bread, i.e. crust colour, crumb texture and taste. A careful balance throughout all the steps of the "manufacturing" cycle is vital to ensure the processes synchronization, in order to produce a consistent and satisfactory loaf of bread. A proper energy management of this process needs to consider such features to ensure a high quality product. As such process monitoring can not be conveniently described using customary specific energy metric (correlating the energy demand to the amount of processed material) or full three-dimensional (3D) physic-based modeling as in computational fluid dynamics (CFD). In this paper, the energy analysis of the system is carried out with a methodology rooted in the family of non parametric approaches. The aim of the study is to identify key performance indicators (KPIs) able to assess the effectiveness of the energy "use" along the baking process. Specifically, the identification of KPIs is carried out using Principal Component Analysis (PCA) of the available datasets

Distribution of interleukin-1 receptor complex at the synaptic membrane driven by interleukin-1β and NMDA stimulation

Interleukin-1β (IL-1β) is a pro-inflammatory cytokine that contributes to neuronal injury in various degenerative diseases, and is therefore a potential therapeutic target. It exerts its biological effect by activating the interleukin-1 receptor type I (IL-1RI) and recruiting a signalling core complex consisting of the myeloid differentiation primary response protein 88 (MyD88) and the IL-1R accessory protein (IL-1RAcP). This pathway has been clearly described in the peripheral immune system, but only scattered information is available concerning the molecular composition and distribution of its members in neuronal cells. The findings of this study show that IL-1RI and its accessory proteins MyD88 and IL-1RAcP are differently distributed in the hippocampus and in the subcellular compartments of primary hippocampal neurons. In particular, only IL-1RI is enriched at synaptic sites, where it co-localises with, and binds to the GluN2B subunit of NMDA receptors. Furthermore, treatment with NMDA increases IL-1RI interaction with NMDA receptors, as well as the surface expression and localization of IL-1RI at synaptic membranes. IL-1β also increases IL-1RI levels at synaptic sites, without affecting the total amount of the receptor in the plasma membrane. Our results reveal for the first time the existence of a dynamic and functional interaction between NMDA receptor and IL-1RI systems that could provide a molecular basis for IL-1β as a neuromodulator in physiological and pathological events relying on NMDA receptor activation

An application of data-driven analysis in road tunnels monitoring

In order to comply with the minimum safety requirements imposed by the Directive 2004/54/EC it is of paramount mportance to correctly manage the operation and maintenance of road tunnels. This research describes how Artificial Intelligence techniques can play a supportive role both for maintenance operators in monitoring tunnels and for safety managers in operation. It is possible to extract relevant information from large volumes of data from sensor equipment in an efficient, fast, dynamic and adaptive way and make it immediately usable by those who manage machinery and servicesto aid quick decisions. Carrying out an analysis based on sensors in motorway tunnels, represents an important technological innovation, which would simplify tunnels management activities and therefore the detection of any possible deterioration, while keeping the risk within tolerance limits. The idea involves the creation of an algorithm for the detection of faults by acquiring data in real time from the sensors of tunnel sub-systems and using them to help identify the service state of the tunnel. The AI models are trained on a period of 6 months with one hour time series granularity measured on a road tunnel part of the Italian motorway systems. The verification has been done with reference to a number of recorded sensor faults

Large mediastinal mass of heterotopic thyroid tissue: a case report and review of literature

Salvatore. Incidental detection of a mediastinal mass in a asymptomatic patient poses a not easy diagnostic problem. For solid masses or cysts, histology or cytology is often necessary. Although substernal extension of a cervical goiter is common, totally intrathoracic primary thyroidal mass is unusual. We describe a rare case of heterotopic accessory mediastinal thyroid in a patient completely asymptomatic both for signs of thyroid dysfunction and mechanical compression. Radiological and hormonal 6 and 12 months follow-up is reported

Formation of Low Mass Stars in Elliptical Galaxy Cooling Flows

X-ray emission from hot (T = 10^7 K) interstellar gas in massive elliptical galaxies indicates that 10^{10} M_sun has cooled over a Hubble time, but optical and radio evidence for this cold gas is lacking. We provide detailed theoretical support for the hypothesis that this gas has formed into low luminosity stars. Within several kpc of the galactic center, interstellar gas first cools to T = 10^4 K where it is heated by stellar UV and emits the observed diffuse optical line emission. This cooling occurs at a large number (10^6) of isolated sites. After less than a solar mass of gas has accumulated (10^{-6} M_sun/yr) at a typical cooling site, a neutral (HI or H_2) core develops in the HII cloud where gas temperatures drop to T = 15 K and the ionization level (from thermal X-rays) is very low (x = 10^{-6}). We show that the maximum mass of cores that become gravitationally unstable is only about 2 M_sun. No star can exceed this mass. Fragmentation of collapsing cores produces a population of low mass stars with a bottom-heavy IMF and radial orbits. Gravitational collapse and ambipolar diffusion are rapid. The total mass of star-forming (dust-free) HI or H_2 cores in a typical bright elliptical is only 10^6 M_sun, below current observational thresholds.Comment: 23 pages in AASTEX LaTeX with 8 figures; accepted by Astrophysical Journa

Intravenous methylprednisolone pulses in hospitalised patients with severe COVID-19 pneumonia, A double-blind, randomised, placebo-controlled trial

Rationale: Pulse glucocorticoid therapy is used in hyperinflammation related to coronavirus 2019 (COVID-19). We evaluated the efficacy and safety of pulse intravenous methylprednisolone in addition to standard treatment in COVID-19 pneumonia. Methods: In this multicenter, randomised, double-blind, placebo-controlled trial, 304 hospitalised patients with Covid-19 pneumonia were randomised to receive 1 g of methylprednisolone intravenously for 3 consecutive days or placebo in addition to standard dexamethasone. The primary outcome was the duration of the patient hospitalisation, calculated as the time interval between randomisation and hospital discharge without the need of supplementary oxygen. The key secondary outcomes were survival free from invasive ventilation with orotracheal intubation and overall survival. Results: Overall, 112 of 151 (75.4%) patients in the pulse methylprednisolone arm and 111 of 150 (75.2%) in the placebo arm were discharged from hospital without oxygen within 30 days from randomisation. Median time to discharge was similar in both groups [15 days (95% confidence interval (CI), 13.0 to 17.0) and 16 days (95%CI, 13.8 to 18.2); hazard ratio (HR), 0.92; 95% CI 0.71-1.20; p=0.528]. No significant differences between pulse methylprednisolone and placebo arms were observed in terms of admission to Intensive Care Unit with orotracheal intubation or death (20.0% versus 16.1%; HR, 1.26; 95%CI, 0.74-2.16; p=0.176), or overall mortality (10.0% versus 12.2%; HR, 0.83; 95%CI, 0.42-1.64; p=0.584). Serious adverse events occurred with similar frequency in the two groups. Conclusions: Methylprenisolone pulse therapy added to dexamethasone was not of benefit in patients with COVID-19 pneumonia. Message of the study: Pulse glucocorticoid therapy is used for severe and/or life threatening immuno-inflammatory diseases. The addition of pulse glucocorticoid therapy to the standard low dose of dexamethasone scheme was not of benefit in patients with COVID-19 pneumonia

Development and internal validation of diagnostic prediction models using machine-learning algorithms in dogs with hypothyroidism

IntroductionHypothyroidism can be easily misdiagnosed in dogs, and prediction models can support clinical decision-making, avoiding unnecessary testing and treatment. The aim of this study is to develop and internally validate diagnostic prediction models for hypothyroidism in dogs by applying machine-learning algorithms.MethodsA single-institutional cross-sectional study was designed searching the electronic database of a Veterinary Teaching Hospital for dogs tested for hypothyroidism. Hypothyroidism was diagnosed based on suggestive clinical signs and thyroid function tests. Dogs were excluded if medical records were incomplete or a definitive diagnosis was lacking. Predictors identified after data processing were dermatological signs, alopecia, lethargy, hematocrit, serum concentrations of cholesterol, creatinine, total thyroxine (tT4), and thyrotropin (cTSH). Four models were created by combining clinical signs and clinicopathological variables expressed as quantitative (models 1 and 2) and qualitative variables (models 3 and 4). Models 2 and 4 included tT4 and cTSH, models 1 and 3 did not. Six different algorithms were applied to each model. Internal validation was performed using a 10-fold cross-validation. Apparent performance was evaluated by calculating the area under the receiver operating characteristic curve (AUROC).ResultsEighty-two hypothyroid and 233 euthyroid client-owned dogs were included. The best performing algorithms were naive Bayes in model 1 (AUROC = 0.85; 95% confidence interval [CI] = 0.83-0.86) and in model 2 (AUROC = 0.98; 95% CI = 0.97-0.99), logistic regression in model 3 (AUROC = 0.88; 95% CI = 0.86-0.89), and random forest in model 4 (AUROC = 0.99; 95% CI = 0.98-0.99). Positive predictive value was 0.76, 0.84, 0.93, and 0.97 in model 1, 2, 3, and 4, respectively. Negative predictive value was 0.89, 0.89, 0.99, and 0.99 in model 1, 2, 3, and 4, respectively.DiscussionMachine learning-based prediction models were accurate in predicting and quantifying the likelihood of hypothyroidism in dogs based on internal validation performed in a single-institution, but external validation is required to support the clinical applicability of these models