Industrial circular water use practices through the application of a conceptual water efficiency framework in the process industry.

Increased industrial water demand and resource depletion require the incorporation of sustainable and efficient water and wastewater management solutions in the industrial sector. Conventional and advanced treatment technologies, closed-water loops at different levels from an industrial process to collaborative networks among industries within the same or another sector and digital tools and services facilitate the materialization of circular water use practices. To this end, the scope of this paper is the application of the Conceptual Water Efficiency Framework (CWEF), which has been developed within the AquaSPICE project aspiring to enhance water circularity within industries in a holistic way.

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.

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.

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.

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.

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.