scispace - formally typeset
Search or ask a question
Author

Shirley Gato-Trinidad

Bio: Shirley Gato-Trinidad is an academic researcher from Swinburne University of Technology. The author has contributed to research in topics: Rainwater harvesting & Water conservation. The author has an hindex of 10, co-authored 24 publications receiving 501 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: A review of the previous laboratory analysis and case studies on the application of the pressure-driven ceramic membrane technology for treatment of industrial wastewaters is presented in this article, which reveals that the efficiency of this technology has been proven in a wide variety of waste water from different industries and activities including pulp and paper, textile, pharmaceutical, petrochemical, food and mining.

221 citations

Journal ArticleDOI
TL;DR: In this paper, the application of Artificial Neural Networks (ANN) and multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors.

209 citations

Journal ArticleDOI
TL;DR: The aim of this study is to improve the understanding of the end uses of water and to assist where to focus water conservation efforts that would be most effective financially and environmentally, and be acceptable to everyone.

67 citations

Journal Article
TL;DR: In this article, a model that continuously simulates the accumulation and wash-off of water quality pollutants in a catchment was developed by integrating two individual models; rainfall-runoff model, and catchment water quality model.
Abstract: Estimation of runoff water quality parameters is required to determine appropriate water quality management options. Various models are used to estimate runoff water quality parameters. However, most models provide event-based estimates of water quality parameters for specific sites. The work presented in this paper describes the development of a model that continuously simulates the accumulation and wash-off of water quality pollutants in a catchment. The model allows estimation of pollutants build-up during dry periods and pollutants wash-off during storm events. The model was developed by integrating two individual models; rainfall-runoff model, and catchment water quality model. The rainfall-runoff model is based on the time-area runoff estimation method. The model allows users to estimate the time of concentration using a range of established methods. The model also allows estimation of the continuing runoff losses using any of the available estimation methods (i.e., constant, linearly varying or exponentially varying). Pollutants build-up in a catchment was represented by one of three pre-defined functions; power, exponential, or saturation. Similarly, pollutants wash-off was represented by one of three different functions; power, rating-curve, or exponential. The developed runoff water quality model was set-up to simulate the build-up and wash-off of total suspended solids (TSS), total phosphorus (TP) and total nitrogen (TN). The application of the model was demonstrated using available runoff and TSS field data from road and roof surfaces in the Gold Coast, Australia. The model provided excellent representation of the field data demonstrating the simplicity yet effectiveness of the proposed model. Keywords—Catchment, continuous pollutants build-up, pollutants wash-off, runoff, runoff water quality model

37 citations

Journal ArticleDOI
TL;DR: In this paper, the authors evaluated the performance of Reverse Osmosis (RO) plant as a post-treatment process in mining operations in Victoria, Australia, and showed that the RO unit significantly improves the quality of the final permeate before discharged to surface waters.
Abstract: Reverse Osmosis (RO) membrane has been used for treatment and purification of industrial wastewaters including those from the mining industry before being discharged to receiving body or reuse for applications that are fit for purpose. This study evaluates the performance of Reverse Osmosis (RO) plant as a post-treatment process in mining operations in Victoria, Australia. The data analysis shows that the RO unit significantly improves the quality of the final permeate before discharged to surface waters. Considering average rejection efficiency for the entire evaluated period, turbidity, total dissolved solids (TDS), Antimony, Arsenic, Nickel, Zinc and Iron concentrations are reduced by 85 %, 96 %, 95 %, 66 %, 82 %, 48 % and 10 %, respectively in the RO permeate compared to the feed water. Although the quality of the RO permeate was in a desirable condition in most days of the evaluated years, TDS concentrations on the October 11 and 20,2016 and November 14, 2017 were higher than the limits specified by Environmental Protection Authority (EPA) Victoria. Anomalies regarding antimony levels in RO permeate occurred in September and November 2016 as well as August 2017 due to inconsistency in the RO feed quality. This resulted in fouling of RO membranes and contributed to discharge non-compliance with EPA licence conditions on TDS and antimony. Discharge to waterways was suspended over the period when TDS and antimony contents were above the EPA guidelines. Changes in the pre-treatment reduced the turbidity of the feed water and improved the performance of the RO system to comply with the discharge guidelines.

30 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: A critical assessment of the discourse that surrounds emerging approaches to urban water management and infrastructure provision is provided to highlight the limitations and strengths in the current lines of argument and point towards unaddressed complexities in the transformational agendas advocated by SUWM proponents.

347 citations

Journal ArticleDOI
01 Oct 2018-Water
TL;DR: In this paper, the state-of-the-art machine learning models for both long-term and short-term floods are evaluated and compared using a qualitative analysis of robustness, accuracy, effectiveness and speed.
Abstract: Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction of the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task.

326 citations

Journal ArticleDOI
08 Jul 2021
TL;DR: In this paper, the authors comprehensively and critically review and discuss these methods in terms of used agents/adsorbents, removal efficiency, operating conditions, and the pros and cons of each method.
Abstract: Removal of heavy metal ions from wastewater is of prime importance for a clean environment and human health. Different reported methods were devoted to heavy metal ions removal from various wastewater sources. These methods could be classified into adsorption-, membrane-, chemical-, electric-, and photocatalytic-based treatments. This paper comprehensively and critically reviews and discusses these methods in terms of used agents/adsorbents, removal efficiency, operating conditions, and the pros and cons of each method. Besides, the key findings of the previous studies reported in the literature are summarized. Generally, it is noticed that most of the recent studies have focused on adsorption techniques. The major obstacles of the adsorption methods are the ability to remove different ion types concurrently, high retention time, and cycling stability of adsorbents. Even though the chemical and membrane methods are practical, the large-volume sludge formation and post-treatment requirements are vital issues that need to be solved for chemical techniques. Fouling and scaling inhibition could lead to further improvement in membrane separation. However, pre-treatment and periodic cleaning of membranes incur additional costs. Electrical-based methods were also reported to be efficient; however, industrial-scale separation is needed in addition to tackling the issue of large-volume sludge formation. Electric- and photocatalytic-based methods are still less mature. More attention should be drawn to using real wastewaters rather than synthetic ones when investigating heavy metals removal. Future research studies should focus on eco-friendly, cost-effective, and sustainable materials and methods.

279 citations

Journal ArticleDOI
TL;DR: BDM exhibited an excellent dye removal rate, stable flux and great antifouling capacity, on the ground that adsorption saturation and foulant may be alleviated "online and in-situ" by the enzymatic degradation.

263 citations

Journal ArticleDOI
TL;DR: In this paper, a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011 The predictive variables for the ELM model were the rainfall and mean, minimum, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific DecadalOscillation, Southern Ann

243 citations