A time series model for influent temperature estimation: application to dynamic temperature modelling of an aerated lagoon

Heat Recovery from Wastewater—A Review of Available Resource

The EU Directive 2018/2001 recognized wastewater as a renewable heat source. Wastewater from domestic, industrial and commercial developments maintains considerable amounts of thermal energy after discharging into the sewer system. It is possible to recover this heat by using technologies like heat exchangers and heat pumps; and to reuse it to satisfy heating demands. This paper presents a review of the literature on wastewater heat recovery (WWHR) and its potential at different scales within the sewer system, including the component level, building level, sewer pipe network level, and wastewater treatment plant (WWTP) level. A systematic review is provided of the benefits and challenges of WWHR across each of these levels taking into consideration technical, economic and environmental aspects. This study analyzes important attributes of WWHR such as temperature and flow dynamics of the sewer system, impacts of WWHR on the environment, and legal regulations involved. Existing gaps in the WWHR field are also identified. It is concluded that WWHR has a significant potential to supply clean energy at a scale ranging from buildings to large communities and districts. Further attention to WWHR is needed from the research community, policymakers and other stakeholders to realize the full potential of this valuable renewable heat source.

CFD model to characterize the physical processes of energy transfer in a secondary facultative lagoon

In this paper, the energy transfer phenomena of a secondary facultative lagoon and its relationship with environmental conditions such as wind and solar radiation were studied. To this end, a three-dimensional computational fluid dynamics model was constructed within a conventional facultative lagoon located in the municipality of Ginebra-Valle del Cauca, Colombia. The model included the transport of suspended solids from inlet to outlet, the penetration of incident solar radiation into the body of water, surface wind, the transfer of heat through lateral walls and the temperature profiles of the wastewater. The model was validated using tracer studies and experimental temperature data. This demonstrated that simulated data of temperature and solar radiation penetration have an acceptable degree of consistency compared with the experimental data. The model demonstrated that temperature profiles were significantly affected by the wind at 0.45 m and 1.40 m depth, while no effect on the surface was obtained.

Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia

The function of a sewage treatment plant is to treat the sewage to acceptable standards before being discharged into the receiving waters. To design and operate such plants, it is necessary to measure and predict the influent flow rate. In this research, the influent flow rate of a sewage treatment plant (STP) was modelled and predicted by autoregressive integrated moving average (ARIMA), nonlinear autoregressive network (NAR) and support vector machine (SVM) regression time series algorithms. To evaluate the models' accuracy, the root mean square error (RMSE) and coefficient of determination (R²) were calculated as initial assessment measures, while relative error (RE), peak flow criterion (PFC) and low flow criterion (LFC) were calculated as final evaluation measures to demonstrate the detailed accuracy of the selected models. An integrated model was developed based on the individual models' prediction ability for low, average and peak flow. An initial assessment of the results showed that the ARIMA model was the least accurate and the NAR model was the most accurate. The RE results also prove that the SVM model's frequency of errors above 10% or below - 10% was greater than the NAR model's. The influent was also forecasted up to 44 weeks ahead by both models. The graphical results indicate that the NAR model made better predictions than the SVM model. The final evaluation of NAR and SVM demonstrated that SVM made better predictions at peak flow and NAR fit well for low and average inflow ranges. The integrated model developed includes the NAR model for low and average influent and the SVM model for peak inflow.

Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC

This study presents an approach for obtaining realization sets of parameters for nitrogen removal in a pilot-scale waste stabilization pond (WSP) system. The proposed approach was designed for optimal parameterization, local sensitivity analysis, and global uncertainty analysis of a dynamic simulation model for the WSP by using the R software package Flexible Modeling Environment (R-FME) with the Markov chain Monte Carlo (MCMC) method. Additionally, generalized likelihood uncertainty estimation (GLUE) was integrated into the FME to evaluate the major parameters that affect the simulation outputs in the study WSP. Comprehensive modeling analysis was used to simulate and assess nine parameters and concentrations of ON-N, NH₃-N and NO₃-N. Results indicate that the integrated FME-GLUE-based model, with good Nash–Sutcliffe coefficients (0.53–0.69) and correlation coefficients (0.76–0.83), successfully simulates the concentrations of ON-N, NH₃-N and NO₃-N. Moreover, the Arrhenius constant was the only parameter sensitive to model performances of ON-N and NH₃-N simulations. However, Nitrosomonas growth rate, the denitrification constant, and the maximum growth rate at 20 °C were sensitive to ON-N and NO₃-N simulation, which was measured using global sensitivity.

An integrated physical and biological model for anaerobic lagoons

A computational fluid dynamics (CFD) model that integrates physical and biological processes for anaerobic lagoons is presented. In the model development, turbulence is represented using a transition k-ω model, heat conduction and solar radiation are included in the thermal model, biological oxygen demand (BOD) reduction is characterized by first-order kinetics, and methane yield rate is expressed as a linear function of temperature. A test of the model applicability is conducted in a covered lagoon digester operated under tropical climate conditions. The commercial CFD software, ANSYS-Fluent, is employed to solve the integrated model. The simulation procedures include solving fluid flow and heat transfer, predicting local resident time based on the converged flow fields, and calculating the BOD reduction and methane production. The simulated results show that monthly methane production varies insignificantly, but the time to achieve a 99% BOD reduction in January is much longer than that in July.

Time series in analysis of yerba-mate biennial growth modified by environment

To assess differences in the lag-effect pattern in the relationship between yerba-mate biennial growth and environmental factors, a time-series analysis was performed. A generalized Poisson regression model was used to control time trends, temperature, growing degree days (GDD), rainfalls and night length (NL). It was hypothesized that the active growth and growth pauses in yerba-mate are controlled endogenously and modified by environment, and that genders would respond differently to environmental modifications. The patterns in the lag effect from the distributed-lag models were similar to those of time-series models with meteorological data means with lag = 0. GDD and NL were principal factors affecting biennial yerba-mate shoot elongation and the number of green leaves of females grown in monoculture, besides their significant effects on metamer emission and leaf area in males grown in monoculture. NL also had a significant influence on shoot elongation and leaf area of both genders grown in forest understorey (FUS), indicating that yerba-mate growth is synchronized by an internal clock sensitive to temperature adjustments. The morphological plasticity and the adaptation efforts of yerba-mate were more pronounced in monoculture than in FUS. Sexual dimorphism was expressed-males were more sensitive to environmental changes than females, especially in monoculture. Growth modifications were much more intense when plants were grown in a cultivation system that is less like yerba-mate natural habitat (monoculture) than in one resembling its natural habitat (FUS). Our data support the ecological specialization theory.