ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Construction of the Midge History Match Model

Imperial Users onl

4D Seismic History Matching Incorporating Unsupervised Learning

The work discussed and presented in this paper focuses on the history matching of reservoirs by integrating 4D seismic data into the inversion process using machine learning techniques. A new integrated scheme for the reconstruction of petrophysical properties with a modified Ensemble Smoother with Multiple Data Assimilation (ES-MDA) in a synthetic reservoir is proposed. The permeability field inside the reservoir is parametrised with an unsupervised learning approach, namely K-means with Singular Value Decomposition (K-SVD). This is combined with the Orthogonal Matching Pursuit (OMP) technique which is very typical for sparsity promoting regularisation schemes. Moreover, seismic attributes, in particular, acoustic impedance, are parametrised with the Discrete Cosine Transform (DCT). This novel combination of techniques from machine learning, sparsity regularisation, seismic imaging and history matching aims to address the ill-posedness of the inversion of historical production data efficiently using ES-MDA. In the numerical experiments provided, I demonstrate that these sparse representations of the petrophysical properties and the seismic attributes enables to obtain better production data matches to the true production data and to quantify the propagating waterfront better compared to more traditional methods that do not use comparable parametrisation techniques

Peanut Shell for Energy: Properties and Its Potential to Respect the Environment

The peanut (Arachys hypogaea) is a plant of the Fabaceae family (legumes), as are chickpeas, lentils, beans, and peas. It is originally from South America and is used mainly for culinary purposes, in confectionery products, or as a nut as well as for the production of biscuits, breads, sweets, cereals, and salads. Also, due to its high percentage of fat, peanuts are used for industrialized products such as oils, flours, inks, creams, lipsticks, etc. According to the Food and Agriculture Organization (FAO) statistical yearbook in 2016, the production of peanuts was 43,982,066 t, produced in 27,660,802 hectares. Peanuts are grown mainly in Asia, with a global production rate of 65.3%, followed by Africa with 26.2%, the Americas with 8.4%, and Oceania with 0.1%. The peanut industry is one of the main generators of agroindustrial waste (shells). This residual biomass (25–30% of the total weight) has a high energy content that is worth exploring. The main objectives of this study are, firstly, to evaluate the energy parameters of peanut shells as a possible solid biofuel applied as an energy source in residential and industrial heating installations. Secondly, different models are analysed to estimate the higher heating value (HHV) for biomass proposed by different scientists and to determine which most accurately fits the determination of this value for peanut shells. Thirdly, we evaluate the reduction in global CO2 emissions that would result from the use of peanut shells as biofuel. The obtained HHV of peanut shells (18.547 MJ/kg) is higher than other biomass sources evaluated, such as olive stones (17.884 MJ/kg) or almond shells (18.200 MJ/kg), and similar to other sources of biomass used at present for home and industrial heating applications. Different prediction models of the HHV value proposed by scientists for different types of biomass have been analysed and the one that best fits the calculation for the peanut shell has been determined. The CO2 reduction that would result from the use of peanut shells as an energy source has been evaluated in all production countries, obtaining values above 0.5 ‰ of their total emissions