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Proper hydraulic simulation models, which are fundamental to analyse a water distribution system, require a calibration procedure. This paper proposes a multi-objective procedure to calibrate water demands and pipe roughness distribution in the context of an ill-posed problem, where the number of measurements is smaller than the number of variables. The proposed methodology consists of a two-steps procedure based on a genetic algorithm. Firstly, several runs of the calibrator are performed and the corresponding pressure and flow-rates values are averaged to overcome the non-uniqueness of the solutions problem. Secondly, the final calibrated model is achieved using the calibrator with the average values of the previous step as the reference condition. Therefore, the procedure enables to obtain physically based hydraulic parameters. Moreover, several hydraulic models are investigated to assess their performance on this optimisation procedure. The considered models are based either on concentrated at nodes or distributed along pipes demands approach, but also either on demand driven or pressure driven approach. Results show the reliability of the final calibrated model in the context of the ill-posed problem. Moreover, it is observed the overall better performance of the pressure driven approach with distributed demand in scarce pressure condition.

Modelling heat load is a crucial challenge for the proper management of heat production and distribution. Several studies have tackled this issue at building and urban levels, however, the current scale of interest is shifting to the district level due to the new paradigm of the smart system. This study presents a stochastic procedure to model district heat load with a different number of buildings aggregation. The proposed method is based on a superimposition approach by analysing the seasonal component using a linear regression model on the outdoor temperature and the intra-daily component through a bi-parametric distribution of different times of the day. Moreover, an empirical relationship, that estimates the demand variation given the average demand together with a user aggregation coefficient, is proposed. To assess the effectiveness of the proposed methodology, the study of a group of residential users connected to the district heating system of Bozen-Bolzano is carried out. In addition, an application on a three-day prevision shows the suitability of this approach. The final purpose is to provide a flexible tool for district heat load characterisation and prevision based on a sample of time series data and summary information about the buildings belonging to the analysed district.

Mucus hypersecretion contributes to lung function impairment observed in COPD (chronic obstructive pulmonary disease), a tobacco smoking-related disease. A detailed mucus hypersecretion adverse outcome pathway (AOP) has been constructed from literature reviews, experimental and clinical data, mapping key events (KEs) across biological organisational hierarchy leading to an adverse outcome. AOPs can guide the development of biomarkers that are potentially predictive of diseases and support the assessment frameworks of nicotine products including electronic cigarettes. Here, we describe a method employing manual literature curation supported by a focused automated text mining approach to identify genes involved in 5 KEs contributing to decreased lung function observed in tobacco-related COPD. KE genesets were subsequently confirmed by unsupervised clustering against 3 different transcriptomic datasets including (1) in vitro acute cigarette smoke and e-cigarette aerosol exposure, (2) in vitro repeated incubation with IL-13, and (3) lung biopsies from COPD and healthy patients. The 5 KE genesets were demonstrated to be predictive of cigarette smoke exposure and mucus hypersecretion in vitro, and less conclusively predict the COPD status of lung biopsies. In conclusion, using a focused automated text mining and curation approach with experimental and clinical data supports the development of risk assessment strategies utilising AOPs.

Objectives Cigarette smoke aqueous aerosol extracts (AqE) have been used for assessing tobacco products, particularly with in vitro models such as oxidative stress and inflammation. These test articles can be generated easily, but there are no standardised methods for the generation and characterisation or stability. We investigated the effects of pro-oxidant smoke-derived chemicals by using 3R4F AqE generated under standardised conditioning and smoking regimes and assessed the stability over 31-week timeframe. Twenty batches generated from ten puffs per cigarette bubbled through 20 ml cell culture media were used fresh and thawed from frozen aliquots stored at – 80 ºC. Results Nicotine levels quantified by gas chromatography/mass spectrometry and optical density at 260 nm showed chemical and physical stability from week 0 (fresh sample) to weeks 1, 4, 8 and 31 (frozen samples). No significant change in H292 human bronchial epithelial cell viability or oxidative stress were observed between fresh AqE at week 0 and frozen AqE at 31 weeks. AqEs generated by our protocol were stable for up to 31 weeks for all tested end points, suggesting that it may not be necessary to use freshly generated AqE for each study, thus reducing batch-to-batch variability.

Peak water demand is one of the most stringent operative conditions for a Water Distribution System (WDS), not only for the intensity of the event itself, but also for its recurring nature. The estimation of the maximum water demand is a crucial aspect in both the design and management processes. Studies in the past have tackled this issue with deterministic approaches, even if peak phenomena are distinctly random. In this work, probabilistic models have been developed to study and forecast the daily maximum residential water demand. Some probability distributions have been tested by means of statistical inferences on different data samples related to three monitored WDS. The parameter estimations of the proposed equations have been related to the number of supplied users. Furthermore, this work investigates time scaling effects on the effectiveness of the proposed distributions and relations. Corrective factors that take into account the effect of time averaging step on the above-mentioned parameters have been proposed.

In water networks characterized by a significant variation in ground elevations the necessity of pumping water in some areas is complicated by a conflicting requirement to reduce excess pressures in other areas. This and the increasing cost of electricity has led to the use of Pumps-operating-As-Turbines (PATs) devices that can reduce pressure (and leakage) whilst harvesting energy. This paper presents a methodology for optimal water distribution system (WDS) management, driving the optimization by minimizing the surplus pressure at network nodes and the operational pumping costs and maximizing the income generated through energy recovery. The method is based on a highly parallelized Evolutionary Algorithm, employing an hydraulic solver to evaluate hydraulic constraints. Water demands at network nodes are considered as uncertain variables modelled by using a probabilistic approach in order to take into account unknown future demands. The approach is demonstrated in different case studies. Results obtained highlight that the economic benefits of installing PATs for energy recovery in conjunction with a combined pump-scheduling and pressure management regime is especially related to the input network characteristics. Further analysis of the importance of the probabilistic approach and of the influence of the interval time step adopted for the optimization has been evaluated.