Fuel Consumption Monitoring for Travel Demand Modeling

The purpose of this research is to investigate the impact of fuel consumption patterns on travel demand estimation. This paper evaluates and calibrates travel demand estimation by CUBE software and its relation to fuel consumption, with the use of data provided by Sharif University, for the city of Shiraz, Iran in 1999. This research proves the presence strong correlations between vehicle fuel use and the trip's generation process by exogenous and endogenous variables. The effect of energy consumption patterns on generation and distribution stage of travel demand modeling based on inverse ability has been considered in the present model

Application of smoothed particle hydrodynamics in evaluating the performance of coastal retrofit structures

This study develops an accurate numerical tool for investigating optimal retrofit configurations in order to minimize wave overtopping from a vertical seawall due to extreme climatic events and under changing climate. A weakly compressible smoothed particle hydrodynamics (WCSPH) model is developed to simulate the wave-structure interactions for coastal retrofit structures in front of a vertical seawall. A range of possible physical configurations of coastal retrofits including re-curve wall and submerged breakwater are modelled with the numerical model to understand their performance under different wave and structural conditions. The numerical model is successfully validated against laboratory data collected in 2D wave flume at Warwick Water Laboratory. The findings of numerical modelling are in good agreement with the laboratory data. The results indicate that recurve wall is more effective in mitigating wave overtopping and provides more resilience to coastal flooding in comparison to base-case (plain vertical wall) and submerged breakwater retrofit

Application of natural biodegradable fiber as biofilm medium and carbon source in DEnitrifying AMmonium OXidation (DEAMOX) process for nitrogen removal from wastewater

Nitrite (NO2−) accumulation and retaining of anammox bacteria are two decisive factors for stable operation of partial denitrification coupled with the anammox process. Denitrifying ammonium oxidation (DEAMOX) process has been regarded as a promising method for nitrogen removal from wastewater containing both ammonium (NH4+) and nitrate (NO3−) pollutants simultaneously. However, performance and efficiency of DEAMOX process in biofilm reactors are still not fully understood. This study successfully implements biodegradable Luffa Cylindrica fiber as both carbon source and biofilm carrier in the DEAMOX system. 87% nitrate-to-nitrite transformation was achieved through the partial denitrification process. An average total nitrogen (TN) removal efficiency of ~98% was obtained with influent NH4+-N and NO3−-N concentration of 100 mg L−1 in an up-flow packed bed biofilm reactor. The Field Emission-Scanning Electron Microscopy (FE-SEM) analysis indicated that for the proposed conditions, the DEAMOX packed bed reactor favor biofilm granulation developed on L. cylindrica. The results imply that solid phase partial denitrification and granulation of anammox bacteria were achieved using L. cylindrica as a carbon source and biofilm carrier, respectively. Complete NO3− removal observed in this study supports the hypothesis that solid carbon source can support denitrification of NO3− produced through the anammox process

The effects of porous baffles on the hydraulic performance of sediment retention ponds — a numerical modelling study

This study develops a numerical model for investigating the hydraulic characteristics of a retention pond with porous baffles. The numerical model is developed using the Reynolds-averaged Navier-Stokes equations (RANS) with k-εturbulence closure model. The model is successfully validated using physical modelling measurements. The proposed model is used to investigate the key mechanisms that govern and influence the hydraulic efficiency of retention ponds with porous baffles. Three configurations with varying numbers and locations of baffles are simulated. The numerical results are analyzed by comparison of velocity fields, tracer transport patterns, and associated residence time distributions (RTDs) across all the simulation scenarios. It was found that the porous baffles effectively improve hydraulic performance by creating uniform flow distribution and dissipating the flow energy, thereby avoiding dead zones and mitigating short-circuiting. Results show that the location of the first baffle plays a critical role in the flow momentum dissipation. Carefully considerations are required to determine the optimal number and positions of baffles in a specific system. The numerical RTDs are in good agreement with the physical modelling data, confirming the positive contribution of porous baffles to the overall hydraulic performance of the pond by extending the average tracer residence time

Application of unsupervised learning and process simulation for energy optimization of a WWTP under various weather conditions

This paper outlines a hybrid modeling approach to facilitate weather-based operation and energy optimization for the largest Italian wastewater treatment plant (WWTP). Two clustering methods, K-means algorithm and Gaussian mixture model (GMM) based on the expectation-maximization (EM) algorithm, were applied to an extensive data set of historical and meteorological records. This study addresses the problem of determining the intrinsic structure of clustered data when no information other than the observed values is available. Two quantitative indexes, namely the Bayesian Information Criterion (BIC) and the Silhouette coefficient using Euclidean distance, as well as two general criteria, were implemented to assess the clustering quality. Furthermore, seven weather-based influent scenarios were introduced to the process simulation model, and sets of aeration strategies are proposed. The results indicate that incorporating weather-based aeration strategies in the operation of WWTP improves plant energy efficiency

Microplastics transport and mixing mechanisms in the nearshore region

Microplastics (MP) are emerging pollutants in the marine environment with potential ecotoxicological effects on littoral and coastal ecosystems. A dominate contributing source of microplastic particles is the fragmentation of macroplastics from manufactured goods, alongside laundered synthetic material, abrasion of vehicle tyres and personal care products. The indiscriminate use of plastic and poor management of plastic waste pose serious threat to ecosystem functionality and resilience. Understanding the key underlying transport and mixing mechanisms which influence the behavior of microplastics and their environmental fate are crucial for identify potential microplastic fate-transport pathways from source to sink. This is fundamental for evaluating microplastic interactions and impact on ecosystems. This paper presents laboratory-based tracer measurements for solute and polyethylene (PE) microplastics in the presence of waves. The tests were undertaken in a wave tank equipped with an active absorption paddle-type wave-maker. Fluorescent dye was used to stain the PE particles using a novel staining technique. Rhodamine dye was used as a proxy for the transport of solute pollutants. The temporal and spatial behavior of both microplastics and solute across the nearshore zone was measured using submersible fiber optic fluorometers. Hydrodynamic conditions were designed to create monochromatic waves with a range of wave steepness Sop = 2 - 5 percent. Tracer measurements were conducted at three locations, seaward of the breaker region, breaker region and inner surf zone to provide a comprehensive understanding of mixing across the nearshore. The dispersion coefficients were determined for both solute and PE particles. The results indicate the dominant role of surface and bed generated turbulence in determining mixing and dispersion influenced by wave breaker type and width of the surf zone. The comparison of tracer data suggests that PE particles, with similar density to water, and the solute tracer have a similar transport and mixing behavior under the influence of waves

Energy optimization of a wastewater treatment plant based on energy audit data : small investment with high return

Ambitious energy targets in the 2020 European climate and energy package have encouraged many stakeholders to explore and implement measures improving the energy efficiency of water and wastewater treatment facilities. Model-based process optimization can improve the energy efficiency of wastewater treatment plants (WWTP) with modest investment and a short payback period. However, such methods are not widely practiced due to the labor-intensive workload required for monitoring and data collection processes. This study offers a multi-step simulation-based methodology to evaluate and optimize the energy consumption of the largest Italian WWTP using limited, preliminary energy audit data. An integrated modeling platform linking wastewater treatment processes, energy demand, and production sub-models is developed. The model is calibrated using a stepwise procedure based on available data. Further, a scenario-based optimization approach is proposed to obtain the non-dominated and optimized performance of the WWTP. The results confirmed that up to 5000 MWh annual energy saving in addition to improved effluent quality could be achieved in the studied case through operational changes only

Copula-based probabilistic assessment of intensity and duration of cold episodes: A case study of Malayer vineyard region

Frost, particularly during the spring, is one of the most damaging weather phenomena for vineyards, causing significant economic losses to vineyards around the world each year. The risk of tardive frost damage in vine-yards due to changing climate is considered as an important threat to the sustainable production of grapes. Therefore, the cold monitoring strategies is one of the criteria with significant impacts on the yields and prosperity of horticulture and raisin factories. Frost events can be characterized by duration and severity. This paper investigates the risk and impacts of frost phenomenon in the vineyards by modeling the joint distribution of duration and severity factors and analyzing the influential parameter’s dependency structure using capabilities of copula functions. A novel mathematical framework is developed within this study to understand the risk and uncertainties associate with frost events and the impacts on yields of vineyards by analyzing the non-linear dependency structure using copula functions as an efficient tool. The developed model was successfully vali-dated for the case study of vineyard in Malayer city of Iran. The copula model developed in this study was shown to be a robust tool for predicting the return period of the frost events

Sina Borzooei's Quick Files

The Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity

The impact of climate change on wastewater treatment plants

How to minimize the impact of climate change on performance of wastewater treatment plants:Investigation, Prediction, Preparation and Resilienc