A5043271942- Wastewater Treatment and Nitrogen Removal
- Water Quality Monitoring and Analysis
- Hydrological Forecasting Using AI
- Wastewater Treatment and Reuse
- Water Systems and Optimization
- Water Quality Monitoring Technologies
- Reservoir Engineering and Simulation Methods
- Advanced Data Processing Techniques
- Algal biology and biofuel production
- Advanced Control Systems Optimization
- Groundwater flow and contamination studies
- Engineering Education and Technology
- Membrane Separation Technologies
- Scientific Computing and Data Management
- Water resources management and optimization
- Extraction and Separation Processes
- Anaerobic Digestion and Biogas Production
- Phosphorus and nutrient management
- Odor and Emission Control Technologies
- Municipal Solid Waste Management
- Advanced Manufacturing and Logistics Optimization
- Energy Load and Power Forecasting
- Smart Grid Energy Management
- Business Process Modeling and Analysis
- Industrial Automation and Control Systems
Hatch (Canada)
2021-2024
Hydromantis Environmental Software Solutions (Canada)
2005-2019
McMaster University
2001
Digital Twins (DTs) are on the rise as innovative, powerful technologies to harness power of digitalisation in WRRF sector. The lack consensus and understanding when it comes definition, perceived benefits technological needs DTs is hampering their widespread development application. Transitioning from traditional modelling practice into DT applications raises a number important questions: When model's predictive acceptable for DT? Which frameworks most suited applications? data structures...
Abstract Wastewater flow forecasting is key for proper management of wastewater treatment plants (WWTPs). However, to predict the amount incoming in WWTPs, engineers face challenges arising from numerous complexities and uncertainties, such as nonlinear precipitation-runoff relationships combined sewer systems, unpredictability due aging infrastructure, frequently inconsistent data quality. To address challenges, a time series analysis model (i.e., autoregressive integrated moving average,...
This paper addresses the problem of synergizing first-principles models with data-driven models. is achieved by building a hybrid model where subspace identification algorithm used to create for residuals (mismatch in outputs generated and plant output) rather than being dynamic process outputs. A continuous stirred tank reactor (CSTR) setup illustrate proposed approach on system. To further evaluate its efficacy, methodology applied batch poly(methyl methacrylate) (PMMA) polymerization...
Autoregressive Integrated Moving Average (ARIMA) is a time series analysis model that can be dated back to 1955. It has been used in many different fields of study analyze and forecast future data points; however, it not widely daily wastewater influent flow. The objective this explore the possibility for treatment plants (WWTPs) utilize ARIMA flow forecasting. To pursue confidently, five stations across North America are validate ARIMA’s performance. These include Woodward, Niagara, Davis,...
Abstract The wastewater industry is currently facing dramatic changes, shifting away from energy-intensive treatment towards low-energy, sustainable technologies capable of achieving energy positive operation and resource recovery. latter will shift the focus to how one could manage extract resources wastewater, as opposed conventional paradigm treatment. Debatable questions arise: can more complex models be calibrated, or additional unknowns introduced? After almost 30 years using...
Microalgae-based tertiary wastewater treatment has the potential to meet stringent effluent phosphorus limits, with added benefit of producing a marketable feedstock. However, lack validated mechanistic models and their implementation in process simulators have limited adoption this technology. In study, an updated lumped pathway metabolic model (Phototrophic-Mixotrophic Process Model, PM2), including both photoautotrophic heterotrophic metabolisms microalgae, was developed predict...
Influent flow rate is a crucial parameter closely related to the plant-wide control of wastewater treatment plants (WWTPs). In this study, random forest (RF) model and multi-layer perceptron (MLP) are developed for hourly influent prediction at confidential WWTP in Canada. Both models perform well on predicting one-step ahead. The coefficient determination (R2) values MLP RF testing data set 0.927 0.925, respectively. Furthermore, multi-step ahead accuracy proposed discussed. To improve...
This paper describes a new anaerobic digestion model for wastewater treatment systems (MantisAD). The has been developed specifically plant-wide modelling. That is, rather than modelling nitrogen as series of fractions other carbonaceous state variables, this includes six dedicated variables. structure makes easier to incorporate into models by simplifying the aerobic/anaerobic interfaces. is complete and initial success with achieved. A comprehensive description including Petersen Matrix...
Anaerobic digestion (AD) is a biological treatment process to stabilize organic solids and produce biogas. If present, sulfate reduced sulfide by anaerobic sulfate-reducing bacteria the can be toxic microorganisms. Here, effect of high initial concentration on AD wastewater sludge was investigated using lab-scale batch experiments. Additionally, systematic mathematical modeling approach applied for insight into experimental results. Cumulative biogas methane production decreased with...
Model-Based Optimum Design of Sequencing Batch Reactors for COD and Nitrogen Removal from a Slaughterhouse WastewaterA dynamic model the activated sludge process was used to analyze optimize operation an SBR treating slaughterhouse wastewater. The existing treatment cycle (duration fill, aeration, mix, decanting wasting periods) found be inadequate meeting effluent requirements under number different loading scenarios. Modelling analysis indicated that aeration phase was...Author(s)Hank...