Particle sensing technology has shown great potential for monitoring particulate matter (PM) with very few temporal and spatial restrictions because of its low cost, compact size, and easy operation. However, the performance of low-cost sensors for PM monitoring in ambient conditions has not been thoroughly evaluated. Monitoring results by low-cost sensors are often questionable. In this study, a low-cost fine particle monitor (Plantower PMS 5003) was colocated with a reference instrument, the Synchronized Hybrid Ambient Real-time Particulate (SHARP) monitor, at the Calgary Varsity air monitoring station from December 2018 to April 2019. The study evaluated the performance of this low-cost PM sensor in ambient conditions and calibrated its readings using simple linear regression (SLR), multiple linear regression (MLR), and two more powerful machine-learning algorithms using random search techniques for the best model architectures. The two machine-learning algorithms are XGBoost and a feedforward neural network (NN). Field evaluation showed that the Pearson correlation (r) between the low-cost sensor and the SHARP instrument was 0.78. The Fligner and Killeen (F–K) test indicated a statistically significant difference between the variances of the PM_2.5 values by the low-cost sensor and the SHARP instrument. Large overestimations by the low-cost sensor before calibration were observed in the field and were believed to be caused by the variation of ambient relative humidity. The root mean square error (RMSE) was 9.93 when comparing the low-cost sensor with the SHARP instrument. The calibration by the feedforward NN had the smallest RMSE of 3.91 in the test dataset compared to the calibrations by SLR (4.91), MLR (4.65), and XGBoost (4.19). After calibrations, the F–K test using the test dataset showed that the variances of the PM_2.5 values by the NN, XGBoost, and the reference method were not statistically significantly different. From this study, we conclude that a feedforward NN is a promising method to address the poor performance of low-cost sensors for PM_2.5 monitoring. In addition, the random search method for hyperparameters was demonstrated to be an efficient approach for selecting the best model structure.

The study provides an overview of Predictive Emissions Monitoring System's (PEMS) research, application, installation, and regulatory framework as well as develops predictive models for NOx emissions from a natural gas fired cogeneration unit using an open source machine learning library, Keras, and open source programming languages, Python and R. Nine neural network based predictive models were trained with 12 086 examples and tested with 3020 examples. The neural network-based models use eight process parameters as inputs to predict NOx emissions. All models meet the regulatory requirements for precision. The best model (32-64-64-64) has four hidden layers and uses the Nadam method for optimization. The best model has a mean absolute error of 0.5982, r-value of 0.9451, and a difference of 0.14% between the measured and predicted emission values using the test dataset. The study demonstrated the feasibility of using open source machine learning library in PEMS development. It also provides guidance to facility operators to develop their own PEMS models for monitoring emissions.

Canada’s in situ oil sands can help meet the global oil demand. Because of the energy-intensive extraction processes, in situ oil sands operations also play a critical role in meeting the global carbon budget. The steam oil ratio (SOR) is an indicator used to measure energy efficiency and assess greenhouse gas (GHG) emissions in the in situ oil sands industry. A low SOR indicates an extraction process that is more energy efficient and less carbon intensive. In this study, we applied machine learning methods for data-driven discovery to a public database, Petrinex, containing operating data from 2015 to 2019 extracted from over 35 million records for 20 in situ oil sands extraction operations. Two unsupervised machine learning methods, including clustering and association rules, showed that the cyclic steam stimulation (CSS) recovery method was less efficient than the steam-assisted gravity drainage (SAGD) recovery method. Chi-square tests showed a statistically significant association between the CSS recovery method and high SOR (p < 0.005). Two association rules suggested that the occurrence of non-condensable gas (NCG) co-injection produced a low SOR. Chi-square tests on the two rules identified a statistically significant relationship between gas co-injection and low SOR (p < 0.005). Association rules also indicated that there was no association between the production regions and SORs. For future in situ oil sands development, decision-makers should consider SAGD as the preferred method because it is less carbon intensive. Existing in situ oil sands projects and future development should explore the possibility of NCG co-injection with steam to reduce steam consumption and consequently reduce GHG emissions from the extraction processes.