Bardia Roghani

Bio: Bardia Roghani is an academic researcher from University of Tehran. The author has contributed to research in topics: Precipitation & Orography. The author has an hindex of 4, co-authored 8 publications receiving 81 citations.

Sewer asset management – state of the art and research needs

Abstract: Sewer asset management gained momentum and importance in recent years due to economic considerations, since infrastructure maintenance and rehabilitation directly represent major investments. Becau...

Life-Cycle Assessment (LCA) of Wastewater Treatment Plants: A Case Study of Tehran, Iran

Abstract: Evaluating the environmental impacts of WWTPs and finding ways for wastewater reuse with minimum damage to our environment and human societies is a matter of vital importance. The objective of this study is to identify the critical sources of environmental impacts in Tehran’s WWTP using life-cycle assessment (LCA) method. Eco-Indicator 99 is selected to perform life-cycle impact assessment (LCIA) using SimaPro 7.0 software. Results show that application of biogas instead of natural gas can make a significant contribution in alleviating the environmental effects of Tehran’s WWTP (e.g., decreasing the negative impacts of fossil fuels about three times). Discharging the effluent into the surface water resources causes considerable damages to the quality of these resources and should be prevented. Instead, using the effluent for agricultural purposes in south of Tehran is a more eco-friendly practice especially from an eutrophication perspective (4% of the previous scenario). In general, the results obtained from implemented case study show that despite some shortcomings such as availability of sufficient and reliable data, LCA is an appropriate environmental system tool capable of streamlining the decision-making process in the wastewater treatment industry in Iran as well as fostering opportunities to achieve sustainability goals.

Risk Assessment of Factors Influencing Non-Revenue Water Using Bayesian Networks and Fuzzy Logic

Abstract: This paper aims to initially, identify effective factors of Non Revenue Water (NRW) and its three major components: apparent losses, real losses and non-revenue authorized consumptions in water distribution networks. In the next step, they should be ranked according to their potential in the reduction of the amount of NRW. Besides, incidence of each NRW components imposes some special economic and social impacts. In the present study, by considering data scarcity and uncertainty of the available data and information, risk assessment of NRW and its components has been done through two stages. The first stage is initiated by designing a questionnaire so as to collect data and information about 41 identified parameters in part of Tehran Water and Wastewater Company as the research pilot. Then, in order to demonstrate the probability relationships between factors influencing NRW components, Bayesian Network (BN) is used. At the end of this stage, the parameters are prioritized in terms of their impact. Through the following stage, consequences of NRW components existence are identified and fuzzified, to consider their uncertainty. After that, based on the risk definition and Fuzzy Inference Systems )FIS( concept, fuzzified probability and consequences are combined and NRW components’ risk is calculated. In this study, the calculated risk of NRW components in the study area are “Moderately High” and equal to 7.05, 6.95 and 6.4 for apparent loss, real loss and non-revenue authorized consumptions, respectively which means that decision makers and managers of this district should take serious actions to reduce the amount of risk.

Artificial Intelligence–Based Optimization of Reverse Osmosis Systems Operation Performance

Abstract: In recent years, reverse osmosis (RO) systems have been highly utilized in industrial processes. One of the most important operational issues of these systems is membrane fouling, which lea...

Bayesian belief networks for predicting drinking water distribution system pipe breaks

Abstract: In this paper, we use Bayesian Belief Networks (BBNs) to construct a knowledge model for pipe breaks in a water zone. To the authors’ knowledge, this is the first attempt to model drinking water distribution system pipe breaks using BBNs. Development of expert systems such as BBNs for analyzing drinking water distribution system data is not only important for pipe break prediction, but is also a first step in preventing water loss and water quality deterioration through the application of machine learning techniques to facilitate data-based distribution system monitoring and asset management. Due to the difficulties in collecting, preparing, and managing drinking water distribution system data, most pipe break models can be classified as “statistical–physical” or “hypothesis-generating.” We develop the BBN with the hope of contributing to the “hypothesis-generating” class of models, while demonstrating the possibility that BBNs might also be used as “statistical–physical” models. Our model is learned from pipe breaks and covariate data from a mid-Atlantic United States (U.S.) drinking water distribution system network. BBN models are learned using a constraint-based method, a score-based method, and a hybrid method. Model evaluation is based on log-likelihood scoring. Sensitivity analysis using mutual information criterion is also reported. While our results indicate general agreement with prior results reported in pipe break modeling studies, they also suggest that it may be difficult to select among model alternatives. This model uncertainty may mean that more research is needed for understanding whether additional pipe break risk factors beyond age, break history, pipe material, and pipe diameter might be important for asset management planning.

Sewer asset management – state of the art and research needs

Abstract: Sewer asset management gained momentum and importance in recent years due to economic considerations, since infrastructure maintenance and rehabilitation directly represent major investments. Becau...