Combining artificial intelligence and building engineering technologies towards energy efficiency: the case of ventilated façades

none6noPurpose Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations Design/methodology/approach The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant. Findings The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity. Originality/value This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parametrize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.Summa, Serena; Mircoli, Alex; Potena, Domenico; Ulpiani, Giulia; Diamantini, Claudia; Di Perna, CostanzoSumma, Serena; Mircoli, Alex; Potena, Domenico; Ulpiani, Giulia; Diamantini, Claudia; Di Perna, Costanz

Spherical Earth analysis and modeling of lithospheric gravity and magnetic anomalies

A comprehensive approach to the lithospheric analysis of potential field anomalies in the spherical domain is provided. It has widespread application in the analysis and design of satellite gravity and magnetic surveys for geological investigation

An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling

Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, “MeatReg”, a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg” was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC–MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: http://elvis.misc.cranfield.ac.uk/SORF/

The Successful Imitation of the Japanese Lean Production System by American Firms: Impact on American Economic Growth

This paper provides some quantitative evidence about the strong links between the Lean Production System (LPS) or equivalently the holistic Just-in-Time/Quality Control (JIT/QC) system and sectoral (micro) economic growth. This evidence is supported by qualitative arguments that present the LPS or the JIT/QC philosophy as a major and fundamental organizational feature of modern economies. Though the implementation of such a system originated in Japan, the USA have been in the process of catching up in the last fifteen years. Subsequently, recently published American sectoral data (for the period between 1958 and 1996) are used to provide ample quantitative evidence of the role the JIT/QC organizational philosophy played in shaping and leading the American macro and sectoral economies in the last 40 years. The implications for the theory of economic growth and economic policy are also briefly stated.Lean Production, Just -in-Time, Quality Control, organization, American, Japanese, transaction costs, sectors, regression, error correction model, stationarity, total factor productivity, labor productivity, economic growth.

Application of probabilistic deep learning models to simulate thermal power plant processes

Deep learning has gained traction in thermal engineering due to its applications to process simulations, the deeper insights it can provide and its abilities to circumvent the shortcomings of classic thermodynamic simulation approaches by capturing complex inter-dependencies. This works sets out to apply probabilistic deep learning to power plant operations using historic plant data. The first study presented, entails the development of a steady-state mixture density network (MDN) capable of predicting effective heat transfer coefficients (HTC) for the various heat exchanger components inside a utility scale boiler. Selected directly controllable input features, including the excess air ratio, steam temperatures, flow rates and pressures are used to predict the HTCs. In the second case study, an encoder-decoder mixturedensity network (MDN) is developed using recurrent neural networks (RNN) for the prediction of utility-scale air-cooled condenser (ACC) backpressure. The effects of ambient conditions and plant operating parameters, such as extraction flow rate, on ACC performance is investigated. In both case studies, hyperparameter searches are done to determine the best performing architectures for these models. Comparisons are drawn between the MDN model versus standard model architecture in both case studies. The HTC predictor model achieved 90% accuracy which equates to an average error of 4.89 W m2K across all heat exchangers. The resultant time-series ACC model achieved an average error of 3.14 kPa, which translate into a model accuracy of 82%