1,721,003 research outputs found
Artificial neural network modeling of full-scale UV disinfection for process control aimed at wastewater reuse
No full textAccurate modeling of wastewater ultraviolet disinfection is fundamental as support for process optimization and control. Detailed modeling of hydrodynamics and fluence rate via computational fluid dynamics, coupled to laboratory studies of inactivation kinetics, are usually the preferred approach for UV disinfection modeling. Despite this approach often provides accurate predictive performance, it requires significantly high computational time, making it unfeasible for real-time process control. In this study, to enable an effective process control, black-box regression models were assessed as a modeling alternative for UV disinfection, synthesizing hydrodynamics, fluence rate and inactivation kinetics. UV disinfection of a full-scale wastewater treatment plant in Italy was monitored for 10 months, measuring influent and effluent E. coli concentration, turbidity, absorbance at 254 nm, temperature and flow rate at different UV doses. Considering the usually observed distribution of effluent E. coli concentration and the zero inflation of the collected dataset, Poisson, zero-inflated Poisson and Hurdle generalized linear models were tested, as well as two-part models coupling a classifier describing the E. coli zero-count events and a regressor estimating the magnitude of E. coli concentrations in positive-count events. The two-part artificial neural network model showed the best predictive performance, being able of both describing nonlinearities and handling the high proportion of null values in the dataset. The deployment of this model to control ultraviolet disinfection was simulated, estimating a plausible 63% energy saving
Soft sensor predictor of E. coli concentration based on conventional monitoring parameters for wastewater disinfection control
Full text linkReal-time acquisition of indicator bacteria concentration at the inlet of disinfection unit is a fundamental support to the control of chemical and ultraviolet wastewater disinfection. Culture-based enumeration methods need time-consuming laboratory analyses, which give results after several hours or days, while newest biosensors rarely provide information about specific strains and outputs are not directly comparable with regulatory limits as a consequence of measurement principles. In this work, a novel soft sensor approach for virtual real-time monitoring of E. coli concentration is proposed. Conventional wastewater physical and chemical indicators (chemical oxygen demand, total nitrogen, nitrate, ammonia, total suspended solids, conductivity, pH, turbidity and absorbance at 254 nm) and flowrate were studied as potential predictors of E. coli concentration relying on data collected from three full-scale wastewater treatment plants. Different methods were compared: (i) linear modeling via ordinary least squares; (ii) ridge regression; (iii) principal component regression and partial least squares; (iv) non-linear modeling through artificial neural networks. Linear soft sensors reached some degree of accuracy, but performances of the artificial neural network based models were by far superior. Sensitivity analysis allowed to prioritize the importance of each predictor and to highlight the site-specific nature of the approach, because of the site-specific nature of relationships between predictors and E. coli concentration. In one case study, pH and conductivity worked as good proxy variables when the occurrence of intense rain events caused sharp increases in E. coli concentration. Differently, in other case studies, chemical oxygen demand, total suspended solids, turbidity and absorbance at 254 nm accounted for the positive correlation between low wastewater quality and E. coli concentration. Moreover, sensitivity analysis of artificial neural network models highlighted the importance of interactions among predictors, contributing to 25 to 30% of the model output variance. This evidence, along with performance results, supported the idea that nonlinear families of models should be preferred in the estimation of E. coli concentration. The artificial neural network based soft sensor deployment for control of peracetic acid disinfectant dosage was simulated over a realistic scenario of wastewater quality recorded by on-line sensors over 2 months. The scenario simulations highlighted the significant benefit of an E. coli soft sensor, which provided up to 57% of disinfectant saving
Development of a soft-sensor for real-time estimation of E. coli concentration at the inlet of wastewater disinfection
No full textMonitoring microbial pollution indicators requires time-consuming laboratory analyses which give results after several hours or days. A big effort is then carried out by researchers in developing sensors for bacteria detection, but so far these technologies need further testing on wastewater applications. In this work, a soft-sensor for real-time monitoring of E. coli concentration is proposed. Conventional wastewater quality indicators are tested as predictors of E. coli concentration. The soft-sensor is calibrated and tested on data from the effluent of a wastewater treatment plant before the disinfection stage. Among the tested models, artificial neural networks showed the best performances over the test data (R2 = 0.80). The soft-sensor predictions were also evaluated over an historical scenario of the predictors, where it proved to be a useful support for real-time detection of E. coli concentration and thus to control the subsequent disinfection process
Immunochemical investigation of CYP 1A and Mn-SOD in Scapharca inaequivalvis exposed to benzo[a]pyrene
No full textBenzo[a]pyrene among PAHs is the most pollutant investigated for its toxicity and carcinogenicity.
In vertebrates, the CYP1A subfamily members represent the main forms of P450 associated
with exposures to PAHs and the P450 dependent oxidative metabolism of B[a]P leads to the production
of ROS (mostly O2
-). About this, SOD represents an appropriate response to oxidative
injury as a mechanism of protection against increasing ROS levels. This research was focused on the
study of the induction of CYP 1A and Mn-SOD, involved in detoxification processes, in hepatopancreas
of Scapharca inaequivalvis (Adriatic bivalve) exposed to B[a]P. Scapharca specimens were
exposed to 0.44 mg B[a]P. Samples were taken at 0 (control), 12, 24, 36 h of exposure. The B[a]P
concentration were analyzed in samples in soft tissues of pooled animals by HPLC. CYP 1A1 and
Mn-SOD, were detected in hepatopancreas, by western blot analysis after SDS-PAGE. Immunopositive
bands were semi-quantified by Quantity One Software. B[a]P content showed rapid increase
(12 h) followed by a similar rapid decrease. CYP 1A (49 kDa) showed major levels after 36 h of
B[a]P exposure. The Mn-SOD imunopositive band (26 kDa) were less significantly expressed after 36
h of exposure only with respect to the control group. In conclusion the increase of CYP 1A levels
in Scapharca suggests an active biotrasformation mechanism due to the MFO system. The decrease
in Mn-SOD expression might be associated with the disease processes. The induction of CYP 1A in
presence of B[a]P might be a reliable and sensitive biomarker of PAH exposure in aquatic organisms.
(Supported by MIPAF contr. No. 6C66)
Phagocyti
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Effetti della salinità su risposte immunitarie della vongola Chamelea gallina (L. 1758).
No full textIn the present study, the effects of differing salinities on immune responses of the clam, Chamelea gallina (L., 1758), were investigated. The animals were kept for 7 days at 28, 34 (control) and 40‰, and total haemocyte count (THC), phagocytic and lysozyme activities were measured. Results show that salinity values far from 34‰ affects significantly functional responses of C. gallina haemocytes
Quantitative Microbial Risk Assessment to support management of ultraviolet wastewater disinfection
No full textA reuse case study in Milan (Italy) was developed to demonstrate how the Quantitative Microbial Risk Assessment (QMRA) can be used to support optimal management of wastewater disinfection through a risk-based approach. The WWTP deliver reclaimed wastewater to irrigation of rice and corn through two canals which cover about 40 km, but it is use also for vegetable gardens both for home consumption and farmer markets. Wastewater is disinfected with ultraviolet (UV) radiation that can be controlled by varying the number of operating UV lamps. The analysis integrates model of (i) pathogen removal along the WWTP treatment train, (ii) the natural pathogen die-off in canals and (iii) the farm-to-fork exposure path to evaluate the optimal UV dosage requirement. The QMRA model of the system will be used to estimate farm workers and crop consumers risk of illness from exposure to salmonella and norovirus, in scenarios with increasing UV disinfection efficiency. The work aims at impacting the traditional management of wastewater UV disinfection in wastewater reuse systems, by providing a quantitative approach to assess the trade-off between health risk and energy consumption coming from the increase in UV dose
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