Soft Sensing of LPG Processes Using Deep Learning.

This study investigates the integration of soft sensors and deep learning in the oil-refinery industry to improve monitoring efficiency and predictive accuracy in complex industrial processes, particularly de-ethanization and debutanization. Soft sensor models were developed to estimate critical variables such as the C2 and C5 contents in liquefied petroleum gas (LPG) after distillation and the energy consumption of distillation columns. The refinery's LPG purification process relies on periodic sampling and laboratory analysis to maintain product specifications. The models were tested using data from actual refinery operations, addressing challenges such as scalability and handling dirty data. Two deep learning models, an artificial neural network (ANN) soft sensor model and an ensemble random forest regressor (RFR) model, were developed. This study emphasizes model interpretability and the potential for real-time updating or online learning. The study also proposes a comprehensive, iterative solution for predicting and optimizing component concentrations within a dual-column distillation system, highlighting its high applicability and potential for replication in similar industrial scenarios.

Data for the project management, life cycle inventory, costings and energy production of a ground-mounted photovoltaic farm in Greece.

Data was collected using standard communication equipment and invoices provided by an established civil construction and renewable energy development and operation company. Data referring to the construction, costings, operation and environmental impacts of a photovoltaic farm were recorded into four distinct Excel files namely: i) Project Management Data, ii) Life Cycle Inventory (LCI), iii) Electricity Generation Data and iv) Operational Cost Data. For the project management, the given quantities of the resources used in each activity could be further combined with the costs from different geographical and time regions to estimate overall project implementation costs for similar projects. The LCI data for the materials and transportation used can set the basis for life cycle assessment modelling of ground-mounted photovoltaic farms of that size and type. The electricity generation data along with meteorological parameters and location coordinates can be further enhanced to predict and manage energy generation and cashflow of expectations installations of this type and size over time. Finally, the data referring to a number of cost categories('maintenance costs', 'operational costs', 'insurance costs' and 'any other costs'), especially combined with the previously mentioned types of data could support a holistic technoeconomic and environmental assessment of comparable commercial photovoltaic installations. In addition, these data can be used for a comparative multi-disciplinary evaluation between photovoltaics and among various renewable electricity generation alternatives and traditional fossil fuel-based options as well.

Changing Dietary Behavior for Better Biodiversity Preservation: A Preliminary Study.

Broadly consumed dietary patterns, such as the European and Western ones, are exerting pressures on biodiversity both in Europe and globally, and shifting toward a sustainable dietary pattern has thus become a must. This paper constitutes a preliminary communication of the results of a research project on the issue. In this study, the pressures of three dietary patterns (European, Western, and Mediterranean) on biodiversity are addressed in terms of land use, water use, greenhouse gas emissions, and eutrophication impact indicators. The environmental impacts are calculated based on a compositional analysis of each dietary pattern and the environmental footprints of the corresponding food groups. Food balance sheets published by the FAO are used as a basis for the compositional analysis, while the environmental footprints of each of the representative food products are retrieved from related life cycle assessment (LCA) studies. The results show that a shift from the European to the Mediterranean dietary pattern would lead to 10 m2/capita/day land savings, 240 L/capita/day water savings, 3 kg CO2/capita/day reduction in greenhouse gas emissions, and 20 gPO4eq/capita/day reductions in eutrophication potential. Likewise, a shift from the Western to the Mediterranean dietary pattern would lead to 18 m2/capita/day land savings, 100 L/capita/day water savings, 4 kg CO2/capita/day reduction in greenhouse gas emissions, and 16 gPO4eq/capita/day reduction in eutrophication potential. Based on these findings, it is clear that this shift is urgently needed as a step toward environmentally sustainable dietary patterns, such as the Mediterranean one, to preserve biodiversity for future generations.

SWAN platform: A web-based tool to support the development of industrial solid waste reuse business models.

The SWAN platform is an integrated suite of online resources and tools for assessing industrial symbiotic opportunities based on solid industrial waste reuse. It has been developed as a digital solid waste reuse platform and is already applied in four countries (Greece, Bulgaria, Albania and Cyprus). The SWAN platform integrates a database with the spatial and technical characteristics of industrial solid waste producers and potential consumers, populated with data from these countries. It also incorporates an inventory of commercially implemented best practices on solid industrial waste reuse. The role of the SWAN platform is to facilitate the development of novel business cases. Towards this end, decision support services, based on a suitable matching algorithm, are provided to the registered users, helping them to identify and assess potential novel business models, based on solid waste reuse, either for an individual industrial unit (source/potential receiver of solid waste) or a specific region.

Data for moisture measurements during vertical absorption in building porous materials such as brick and limestone.

This article contains the datasets obtained from experiments in laboratory related to moisture propagation in building porous materials. The datasets contain moisture measurements and corresponding time measurements during vertical infiltration experiment in brick and limestone samples. Moisture measurements were carried out using a γ-ray device and water volume absorption was recorded by a computer controlled digital scale.