This study addresses the fouling of heat exchangers by coprecipitating inorganic salts. It investigates the effects of nondominant CaCO3 on thermodynamics and kinetics of CaSO4 precipitation as well as on scale structure. Even though precipitating salts coexist in industrial water systems, because of the complexity of the fouling process, research has primarily been concentrated on a single salt precipitation. In addition, all thermodynamic predictive models and softwares only consider the effect of coexisting salts through the ionic strength by Debye−Huckel or modified forms of that. The results of this investigation indicate that the presence of a coprecipitating salt with a common ion affects the solubility constants of the salts, and therefore application of the solubility data obtained under a single salt precipitation is questionable for coprecipitating salts with common ions. Also, the kinetics of precipitation is affected by the presence of a common ion, and the traditional method of kinetic analysis for precipitation of a single sparingly soluble salt is not extendable to conditions where coprecipitation with a common ion effect is present. A comprehensive method of analysis is proposed for coprecipitation with a common ion. Experimental measurements are required to incorporate the effect of common ions in mixed electrolyte systems until such a time that theoretical relationships address this issue. Further study is underway to cover a wider experimental range.

This article contributes to advancing the knowledge on the phenomenon of the most popular short-term rental platforms, Airbnb. By implementing a geographically weighted regression (GWR) and its multiscale form, MGWR, we examine the relationship between Airbnb locations and the core elements of urban tourism including hotels, food and beverages (F&B) venues, as well as access to public transport. This article's contributions are twofold: methodological and empirical. First, the results show that incorporating localities improve overall model performance. It allows us to account for the nuance of each area of interest as the MGWR performs slightly better than the GWR in the case of spatially sparse data. Second, both models show that Airbnbs collocate with hotels supported by various amenities, but Airbnbs also go beyond traditional hotel zones. This analysis highlights and extends the latter where Airbnb concentrations are those for which there are strong associations with F&B establishments and access to public transports. This suggests that Airbnbs might benefit local businesses outside the reach of major tourist zones. However, there is further work to be done to understand whether the economic benefit to the local economy is worth the associated social costs raised by previous studies.

In an era of smart cities, artificial intelligence and machine learning, data is purported to be the ‘new oil’, fuelling increasingly complex analytics and assisting us to craft and invent future cities. This paper outlines the role of what we know today as big data in understanding the city and includes a summary of its evolution. Through a critical reflective case study approach, the research examines the application of urban transport big data for informing planning of the city of Sydney. Specifically, transport smart card data, with its diverse constraints, was used to understand mobility patterns through the lens of the 30 min city concept. The paper concludes by offering reflections on the opportunities and challenges of big data and the promise it holds in supporting data-driven approaches to planning future cities.