| 1. |
- Lundstedt-Enkel, Katrin, et al.
(författare)
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A Statistical Resampling Method To Calculate Biomagnification Factors Exemplified with Organochlorine Data from Herring (Clupea harengus) Muscle and Guillemot (Uria aalge) Egg from the Baltic Sea
- 2005
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Ingår i: ENVIRONMENTAL SCIENCE & TECHNOLOGY. - : American Chemical Society (ACS). - 0013-936X .- 1520-5851. ; 39:21, s. 8395-8402
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Tidskriftsartikel (refereegranskat)abstract
- A novel method for calculating biomagnification factors is presented and demonstrated using contaminant concentration data from the Swedish national monitoring program regarding organochlorine contaminants (OCs) in herring (Clupea harengus) muscle and guillemot (Uria aalge) egg, sampled from 1996 to 1999 from the Baltic Sea. With this randomly sampled ratios (RSR) method, biomagnification factors (BMFRSR) were generated and denoted with standard deviation (0) as a measure of the variation. The BMFRSR were calculated by randomly selecting one guillemot egg out of a total of 29 and one herring out of a total of 74, and the ratio was determined between the concentration of a given OC in that egg and the concentration of the same OC in that herring. With the resampling technique, this was performed 50 000 times for any given OC, and from this new distribution of ratios, BMFRSR for each OC were calculated and given as geometric mean (GM) with GM standard deviation (GMSD) range, arithmetic mean (AM) with AMSD range, and minimum (BMFMIN) as well as maximum (BMFMAX) biomagnification factors. The 14 analyzed OCs were p,p'DDT and its metabolites p,p'DDE and p,p'DDD, polychlorinated biphenyls (PCB congeners: CB28, CB52, CB101, CB118, CB138, CB153, and CB180), hexachlorocyclohexane isomers (alpha-, beta-, and gamma HCH), and hexachlorobenzene (HCB). Multivariate data analysis (MVDA) methods, including principal components analysis (PCA), partial least squares regression (PLS), and PLS discriminant analyses (PLS-DA), were first used to extract information from the complex biological and chemical data generated from each individual animal. MVDA were used to model similarities/dissimilarities regarding species (PCA, PLS-DA), sample years (PLS), and sample location (PLS-DA) to give a deeper understanding of the data that the BMF modeling was based upon. Contaminants that biomagnify, that had BMFRSR significantly higher than one, were p,p'DDE, CB118, HCB, CB138, CB180, CB153, beta HCH, and CB28. The contaminants that did not biomagnify were p,p'DDT, p,p'DDD, alpha HCH, CB101, and CB52. Eventual biomagnification for gamma HCH could not be determined. The BMFRSR for OCs present in herring muscle and guillemot egg showed a broad span with large variations for each contaminant. To be able to make reliable calculations of BMFs for different contaminants, we emphasize the importance of using data based upon large numbers of, as well as well-defined, individuals.
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| 2. |
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| 3. |
- Wang, Dong, et al.
(författare)
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A machine learning framework to improve effluent quality control in wastewater treatment plants
- 2021
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Ingår i: Science of the Total Environment. - : Elsevier. - 0048-9697 .- 1879-1026. ; 784
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Tidskriftsartikel (refereegranskat)abstract
- Due to the intrinsic complexity of wastewater treatment plant (WWTP) processes, it is always challenging to respond promptly and appropriately to the dynamic process conditions in order to ensure the quality of the effluent, especially when operational cost is a major concern. Machine Learning (ML) methods have therefore been used to model WWTP processes in order to avoid various shortcomings of conventional mechanistic models. However, to the best of the authors' knowledge, no ML applications have focused on investigating how operational factors can affect effluent quality. Additionally, the time lags between process steps have always been neglected, making it difficult to explain the relationships between operational factors and effluent quality. Therefore, this paper presents a novel ML-based framework designed to improve effluent quality control in WWTPs by clarifying the relationships between operational variables and effluent parameters. The framework consists of Random Forest (RF) models, Deep Neural Network (DNN) models, Variable Importance Measure (VIM) analyses, and Partial Dependence Plot (PDP) analyses, and uses a novel approach to account for the impact of time lags between processes. Details of the framework are provided along with a demonstration of its practical applicability based on a case study of the Umeå WWTP in Sweden involving a large number of samples (105763) representing the full scale of the plant's operations. Two effluent parameters, Total Suspended Solids in effluent (TSSe) and Phosphate in effluent (PO4e), and thirty-two operational variables are studied. RF models are developed, validated using DNN models as references, and shown to be suitable for VIM and PDP analyses. VIM identifies the variables that most strongly influence TSSe and PO4e, while PDP elucidates their specific effects on TSSe and PO4e. The major findings are: (1) Influent temperature is the most influential variable for both TSSe and PO4e, but it affects them in different ways; (2) PO4e depends strongly on the TSS in aeration basins – higher TSS concentrations in aeration basins generally promote PO4 removal, but excess TSS can have negative effects; (3) In general, the impact of TSS in aeration basins on TSSe and PO4e increases with the distances of the basin from the merging outlet, so more attention should be paid to the TSS concentration in the third or fourth aeration basins than the first and second ones; (4) Returning excessive amounts of sludge through the second return sludge pipe should be avoided because of its adverse impact on TSSe removal. These results could support the development of more advanced control strategies to increase control precision and reduce running costs in the Umeå WWTP and other similarly configured WWTPs. The framework could also be applied to other parameters in WWTPs and industrial processes in general if sufficient high-resolution data are available.
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| 4. |
- Wang, Dong, et al.
(författare)
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A novel data mining framework to investigate causes of boiler failures in waste-to-energy plants
- 2024
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Ingår i: Processes. - : MDPI. - 2227-9717. ; 12:7
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Tidskriftsartikel (refereegranskat)abstract
- Examining boiler failure causes is crucial for thermal power plant safety and profitability. However, traditional approaches are complex and expensive, lacking precise operational insights. Although data-driven approaches hold substantial potential in addressing these challenges, there is a gap in systematic approaches for investigating failure root causes with unlabeled data. Therefore, we proffered a novel framework rooted in data mining methodologies to probe the accountable operational variables for boiler failures. The primary objective was to furnish precise guidance for future operations to proactively prevent similar failures. The framework was centered on two data mining approaches, Principal Component Analysis (PCA) + K-means and Deep Embedded Clustering (DEC), with PCA + K-means serving as the baseline against which the performance of DEC was evaluated. To demonstrate the framework’s specifics, a case study was performed using datasets obtained from a waste-to-energy plant in Sweden. The results showed the following: (1) The clustering outcomes of DEC consistently surpass those of PCA + K-means across nearly every dimension. (2) The operational temperature variables T-BSH3rm, T-BSH2l, T-BSH3r, T-BSH1l, T-SbSH3, and T-BSH1r emerged as the most significant contributors to the failures. It is advisable to maintain the operational levels of T-BSH3rm, T-BSH2l, T-BSH3r, T-BSH1l, T-SbSH3, and T-BSH1r around 527 °C, 432 °C, 482 °C, 338 °C, 313 °C, and 343 °C respectively. Moreover, it is crucial to prevent these values from reaching or exceeding 594 °C, 471 °C, 537 °C, 355 °C, 340 °C, and 359 °C for prolonged durations. The findings offer the opportunity to improve future operational conditions, thereby extending the overall service life of the boiler. Consequently, operators can address faulty tubes during scheduled annual maintenance without encountering failures and disrupting production.
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| 5. |
- Wang, Dong, 1987-
(författare)
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How can data science contribute to a greener world? : an exploration featuring machine learning and data mining for environmental facilities and energy end users
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Human society has taken many measures to address environmental issues. For example, deploying wastewater treatment plants (WWTPs) to alleviate water pollution and the shortage of usable water; using waste-to-energy (WtE) plants to recover energy from the waste and reduce its environmental impact. However, managing these facilities is taxing because the processes and operations are always complex and dynamic. These characteristics hinder the comprehensive and precise understanding of the processes through the conventional mechanistic models. On the other hand, with the development of the Fourth Industrial Revolution, large-volume and high-resolution data from automatic online monitoring have become increasingly obtainable. These data usually reflect abundant detailed information of process activities that can be utilized for optimizing process control. Similarly, data monitoring is also adopted by the resource end users. For example, energy consumption is usually recorded by commercial buildings for optimizing energy consumption behavior, eventually saving running costs and reducing carbon footprint. With the data recorded and retrieved, appropriate data science methods need to be employed to extract the desired information. Data science is a field incorporating formulating data-driven solutions, data preprocessing, analyzing data with particular algorithms, and employing results to support high-level decisions in various application scenarios.The aim of this PhD project is to explore how data science can contribute to a more sustainable world from the perspectives of both improving the operation of environmental engineering processes and optimizing the activities of energy end users. The major work and corresponding results are as follows:(1) (Paper I) An ML workflow consisting of Random Forest (RF) models, Deep Neural Network (DNN) models, Variable Importance Measure (VIM) analyses, and Partial Dependence Plot (PDP) analyses was developed and utilized to model WWTP processes and reveal how operational features impact on effluent quality. The case study was conducted on a full-scale WWTP in Sweden with large data (105,763 samples). This paper was the first ML application study investigating cause-and-effect relationships for full-scale WWTPs. Also, for the first time, time lags between process parameters were treated rigorously for accurate information uncovering. The cause-and-effect findings in this paper can contribute to more sophisticated process control that is more precise and cost-effective. (2) (Paper II) An upgraded workflow was designed to enhance the WWTP cause-and-effect investigation to be more precise, reliable, and comprehensive. Besides RF, two more typical tree-based models, XGBoost and LightGBM, were introduced. Also, two more metrics were adopted for a more comprehensive performance evaluation. A unified and more advanced interpretation method, SHapley Additive exPlanations (SHAP), was employed to aid model comparison and interpret the optimal models more profoundly. Along with the new local findings, this study delivered two significant general findings for cause-and-effect ML implementations in process industries. First, multi-perspective model comparison is vital for selecting a truly reliable model for interpretation. Second, adopting an accurate and granular interpretation method can profit both model comparison and interpretation.(3) (Paper III) A novel workflow was proposed to identify the accountable operational factors for boiler failures at WtE plants. In addition to data preprocessing and domain knowledge integration, it mainly comprised feature space embedding and unsupervised clustering. Two methods, PCA + K-means and Deep Embedding Clustering (DEC), were carried out and compared. The workflow succeeded in fulfilling the objective of a case study on three datasets from a WtE plant in Sweden, and DEC outperformed PCA + K-means for all the three datasets. DEC was superior due to its unique mechanism in which the embedding module and K-means are trained simultaneously and iteratively with the bidirectional information pass.(4) (Paper IV) A two-level (data structure level and algorithm mechanism level) workflow was put forward to detect imperceptible anomalies in energy consumption profiles of commercial buildings. The workflow achieved two objectives – it precisely detected the contextual energy anomalies hidden behind the time variation in the case study; it investigated the combined influence of data structures and algorithm mechanisms on unsupervised anomaly detection for building energy consumption. The overall conclusion was that the contextualization resulted in a less skewed estimation of correlations between instances, and the algorithms with more local perspectives benefited more from the contextualization.
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| 6. |
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| 7. |
- Wang, Dong, et al.
(författare)
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Toward Delicate Anomaly Detection of Energy Consumption for Buildings : Enhance the Performance From Two Levels
- 2022
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 10, s. 31649-31659
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Tidskriftsartikel (refereegranskat)abstract
- Buildings are highly energy-consuming and therefore are largely accountable for environmental degradation. Detecting anomalous energy consumption is one of the effective ways to reduce energy consumption. Besides, it can contribute to the safety and robustness of building systems since anomalies in the energy data are usually the reflection of malfunctions in building systems. As the most flexible and applicable type of anomaly detection approach, unsupervised anomaly detection has been implemented in several studies for building energy data. However, no studies have investigated the joint influence of data structures and algorithms’ mechanisms on the performance of unsupervised anomaly detection for building energy data. Thus, we put forward a novel workflow based on two levels, data structure level and algorithm mechanism level, to effectively detect the imperceptible anomalies in the energy consumption profiles of buildings. The proposed workflow was implemented in a case study for identifying the anomalies in three real-world energy consumption datasets from two types of commercial buildings. Two aims were achieved through the case study. First, it precisely detected the contextual anomalies concealed beneath the time variation of the energy consumption profiles of the three buildings. The performance in terms of areas under the precision-recall curves (AUC_PR) for the three given datasets were 0.989, 0.941, and 0.957, respectively. Second, more broadly, the joint effect of the two levels was examined. On the data level, all four detectors on the contextualized data were superior to their counterparts on the original data. On the algorithm level, there was a consistent ranking of detectors regarding their detecting performances on the contextualized data. The consistent ranking suggests that local approaches outperform global approaches in the scenarios where the goal is to detect the instances deviating from their contextual neighbors rather than the rest of the entire data.
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| 8. |
- Wang, Dong, et al.
(författare)
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Towards better process management in wastewater treatment plants : Process analytics based on SHAP values for tree-based machine learning methods
- 2022
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Ingår i: Journal of Environmental Management. - : Elsevier. - 0301-4797 .- 1095-8630. ; 301
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the mechanisms of pollutant removal in Wastewater Treatment Plants (WWTPs) is crucial for controlling effluent quality efficiently. However, the numerous treatment units, operational factors, and the underlying interactions between these units and factors usually obfuscate the comprehensive and precise understanding of the processes. We have previously proposed a machine learning (ML) framework to uncover complex cause-and-effect relationships in WWTPs. However, only one interpretable ML model, Random forest (RF), was studied and the interpretation method was not granular enough to reveal very detailed relationships between operational factors and effluent parameters. Thus, in this paper, we present an upgraded framework involving three interpretable tree-based models (RF, XGboost and LightGBM), three metrics (R2, Root mean squared error (RMSE), and Mean absolute error (MAE)) and a more advanced interpretation system SHapley Additive exPlanations (SHAP). Details of the framework are provided along with a demonstration of its practical applicability based on a case study of the Umeå WWTP in Sweden. Results show that, for both labels TSSe (Total suspended solids in effluent) and PO4e (Phosphate in effluent), the XGBoost models are optimal whereas the RF models are the least optimal, due to overfitting and polarized fitting. This study has yielded multiple new and significant findings with respect to the control of TSSe and PO4e in the Umeå WWTP and other similarly configured WWTPs. Additionally, this study has produced two important generic findings relating to ML applications for WWTPs (or even other process industries) in terms of cause-and-effect investigations. First, the model comparison should be carried out from multiple perspectives to ensure that underlying details are fully revealed and examined. Second, using a precise, robust, and granular (feature attribution available for individual instances) explanation method can bring extra insight into both model comparison and model interpretation. SHAP is recommended as we found it to be of great value in this study.
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