Predictive Emission Monitoring Research Articles

CO and NOx emissions prediction in gas turbine using a novel modeling pipeline based on the combination of deep forest regressor and feature engineering

The main objective of this study is to estimate carbon oxides (CO) and nitrogen oxides (NOx) emissions from a gas turbine using the predictive emission monitoring systems dataset. First, four methods were developed for feature generation: Principal Component Analysis, t-Distributed Stochastic Neighbor Embedding, Uniform Manifold Approximation and Projection, and Potential of Heat-diffusion for Affinity-based Trajectory Embedding. Then Feature Relevance-based Unsupervised Feature Selection was evaluated for ranking features. With all features generated, the regression models Ridge Regression, Least Absolute Shrinkage and Selection Operator, k-Nearest Neighbor, Cubist Regression, Random Forest, Light Gradient Boosting Machine, Categorical Boosting, and Deep Forest Regression (DFR) were evaluated. The hyperparameters were tuned with Randomized Search Cross Validation using a 5-fold cross-validation (CV) procedure. In this paper, an innovative modeling pipeline based on the mentioned regressors, feature engineering, and effective hyperparameters tuning was proposed. The developed regression models based on feature engineering and hyperparameters tuning were compared using performance criteria, including mean 5-fold CV values of the coefficient of determination (R2) and root mean square error (RMSE). The obtained best results based on mean CV values using the DFR presented R2 values equal to 0.9647 and 0.5355 and RMSE values equal to 0.4245 and 1.4508 for CO emissions, for the training and validation datasets, respectively. On the other hand, DFR presented R2 equal to 0.9866 and 0.4737 and RMSE equal to 1.3876 and 7.6528 for NOx emissions, for the training and validation datasets, respectively.The results show that the proposed DFR model has higher potential for CO and NOx emissions prediction and that feature engineering and hyperparameter tuning has demonstrated impacts on the predictive capacity of the evaluated regression models.

Data Analysis and Symbolic Regression Models for Predicting CO and NOx Emissions from Gas Turbines

Predictive emission monitoring systems (PEMS) are software solutions for the validation and supplementation of costly continuous emission monitoring systems for natural gas electrical generation turbines. The basis of PEMS is that of predictive models trained on past data to estimate emission components. The gas turbine process dataset from the University of California at Irvine open data repository has initiated a challenge of sorts to investigate the quality of models of various machine learning methods to build a model for predicting CO and NOx emissions depending on ambient variables and the parameters of the technological process. The novelty and features of this paper are: (i) a contribution to the study of the features of the open dataset on CO and NOx emissions for gas turbines, which will enable one to more objectively compare different machine learning methods for further research; (ii) for the first time for the CO and NOx emissions, a model based on symbolic regression and a genetic algorithm is presented—the advantage of this being the transparency of the influence of factors and the interpretability of the model; (iii) a new classification model based on the symbolic regression model and fuzzy inference system is proposed. The coefficients of determination of the developed models are: R2=0.83 for NOx emissions, R2=0.89 for CO emissions.

Development of Рredictive Еmission Мonitoring Systems

An overview of world experience in the development and implementation of emission monitoring systems for industrial enterprises, based on mathematical models is presented. The main problems of such systems have been analyzed, their advantages and disadvantages have been revealed. The authors have demonstrated that at Russian enterprises, the introduction of predictive emission monitoring systems was possible at the initial stages of transition to BAT as part of the digital technologies’ integration in production processes.

Predicting CO and NOxemissions from gas turbines: novel data and abenchmark PEMS

Predictive emission monitoring systems (PEMS) are important tools for validation and backing up of costly continuous emission monitoring systems used in gas-turbine-based power plants. Their implementation relies on the availability of appropriate and ecologically valid data. In this paper, we introduce a novel PEMS dataset collected over five years from a gas turbine for the predictive modeling of the CO and NOx emissions. We analyze the data using a recent machine learning paradigm, and present useful insights about emission predictions. Furthermore, we present a benchmark experimental procedure for comparability of future works on the data

Enhanced PEMS Performance and Regulatory Compliance through Machine Learning

<p><em>Modeling technologies</em><em> can pro</em><em>vide strong support to existing emission management systems, by means of what is known as a Predictive Emission Monitoring System (PEMS). These systems do not measure emissions through any hardware device, but use computer models to predict emission concentrations on the ground of process data (e.g., fuel flow, load) and ambient parameters (e.g., air temperature, relative humidity). They actually represent a relevant application arena for the so-called Inferential Sensor technology which has quickly proved to be invaluable in modern process automation and optimization strategies (Qin et al., 1997; Kadlec et al., 2009). While lots of applications demonstrate that software systems provide accuracy comparable to that of hardware-based Continuous Emission Monitoring Systems (CEMS), virtual analyzers are able to offer additional features and capabilities which are often not properly considered by end-users. Depending on local regulations and constraints, PEMS can be exploited either as primary source of emission monitoring or as a back-up of hardware-based CEMS able to validate analyzers’ readings and extend their service factor. PEMS consistency (and therefore its acceptance from environmental authorities) is directly linked to the accuracy and reliability of each parameter used as input of the models. While environmental authorities are steadily opening to PEMS, it is easy to foresee that major recognition and acceptance will be driven by extending PEMS robustness in front of possible sensor failures. Providing reliable instrument fail-over procedures is the main objective of Sensor Validation (SV) strategies. In this work, the capabilities of a class of machine learning algorithms will be presented, showing the results based on tests performed actual field data gathered at a fluid catalytic cracking unit.</em></p>

Assessment and testing of sensor Validation algorithms for environmental monitoring applications

The increasing attention devoted to air quality by legislative, scientific, industrial and public sectors has led to the development of different control strategies for the emission level monitoring. In this scenario, Predictive Emission Monitoring System (PEMS) is able to predict emission concentrations thanks to empirical or first principles models fed by real-time process data provided by measurement sensors. It follows that PEMS consistency (and, crucially, its acceptance from regulations-enforcing agencies) strictly depends on input accuracy and that reliable Sensor Validation (SV) strategies are fundamental. In this work, the capability of two different SV techniques, Feed Forward Neural Networks and Locally Weighted Regression, is tested exploiting a commercial software package (ABB’s IMP) on actual field data from a fluid catalytic cracking unit. The results showed that both techniques are suitable as complement to PEMS applications, but Locally Weighted Regression results are preferable for performance, economic and operating reasons.

B. Indonesia

In January, the minister of environment and forestry and the Danish ambassador for Indonesia conducted a meeting to discuss future outcomes for co-operation between Indonesia and Denmark involving public and private entities. Indonesia and Denmark, through the Danish International Development Agency, have pursued co-operation that focuses on the Environmental Support Program since 2015. Denmark has supported Indonesia in the areas of strategic environmental assessment, environmental impact assessment, and environmental taxes and fines. The program has allowed Indonesia to develop instruments on strategic environmental assessment and an environmental tax and has included Act no. 32/2009 on Environmental Protection and Management. Indonesia and Denmark’s future co-operation will focus on safeguard instruments that include planning and evaluating effective programs and developing strategic environmental assessment, environmental impact assessment, and economic instruments. These instruments are to be implemented in 2018 (<http://www.menlh.go.id/pertemuan-menteri-lhk-ri-dengan-dubes-denmark/>). In March, Indonesia’s Ministry of Environment and Forestry, the Embassy of Denmark, and the Hyprowira Company conducted a focus group discussion on the use of a predictive emission monitoring system in the ministry’s office. The participants of the focus group included government officers, industrial workers, and academic communities. The focus group aimed at increasing compliance with the emission monitoring system and at informing the public on the results of a pilot project on the implementation of emission monitoring in Indonesia, which is supported by Denmark. It also informed the participants on new rules and technology related to the monitoring of emissions (<http://www.menlh.go.id/pemantauan-emisi-industri-melalui-teknologi-predictive-emission-monitoring-system-pems/>).

A performance study of PEMS applied to the Hsinta power station of Taipower

Abstract Predictive emission monitoring systems (PEMS) have been shown to be a promising technology for calculating the emissions from gas-fired stationary sources. One of the first demonstration PEMS in Taiwan was installed and operated successfully at the Hsinta power plant of the Taiwan Power Company (Taipower) in 2000. To improve the accuracy of this PEMS model, further studies were conducted at the same combined cycle gas turbine (CCGT) unit #1 of the Hsinta power plant. To optimize the NOX PEMS model, several functions were progressively developed including a log model, a one-level linear model, a three-level linear model, a 15-variable linear model, and a 15-variable with start-up mode model. The latter gave the relative accuracies (RA) and correlation coefficients between PEMS and a continuous emission monitoring system (CEMS) in the range of 3.38–14.77% and 0.955–0.989, respectively, which fulfill the criteria of the USEPA draft on PEMS performance specifications. Similarly, this model demonstrated RAs for low, medium, high level, and a three-level average less than 20% which meet CEMS requirements set out by Taiwan EPA.

Applicability of the PEMS technique for simplified NO monitoring on board ships

Abstract The performance of a predictive emission monitoring system (PEMS) as a technique for NOx monitoring on medium speed marine diesel engines has been evaluated for 16 similar engines on four different ships. The PEMS function tested measured O2 concentration in the exhaust gas, engine load, combustion air temperature and humidity, and barometric pressure to calculate the NOx concentration. Emission measurements were carried out by means of a conventional continuous emission monitoring system (CEMS) and the measured NOx concentrations were compared with those calculated by the PEMS function. For 11 of the 16 engines, the average error between measured and calculated NOx concentration was Bearing in mind the performance criteria for using PEMS at land-based installations, the results from this study are promising. Viewed as a single data set of 56 h with 16 separate engine comparisons between CEMS and PEMS, the data set shows a relative accuracy of 14.5% i.e. within the 20% requirement of the US Environmental Protection Agency. In light of the increased interest and international guidelines for continuous NOx monitoring on board ships, the PEMS technique can offer a simple but cost-effective option.

A Feasibility Study on the Predictive Emission Monitoring System Applied to the Hsinta Power Plant of Taiwan Power Company

The continuous emission monitoring system (CEMS) can monitor flue gas emissions continuously and instantaneously. However, it has the disadvantages of enormous cost, easily producing errors in sampling periods of bad weather, lagging response in variable ambient environments, and missing data in daily zero and span tests and maintenance. The concept of a predictive emission monitoring system (PEMS) is to use the operating parameters of combustion equipment through thermodynamic or statistical methods to construct a mathematic model that can predict emissions by a computer program. The goal of this study is to set up a PEMS in a gas-fired combined cycle power generation unit at the Hsinta station of Taiwan Power Co. The emissions to be monitored include nitrogen oxides (NOx) and oxygen (O2) in flue gas. The major variables of the predictive model were determined based on the combustion theory. The data of these variables then were analyzed to establish a regression model. From the regression results, the influences of these variables are discussed and the predicted values are compared with the CEMS data for accuracy. In addition, according to the cost information, the capital and operation and maintenance costs for a PEMS can be much lower than those for a CEMS.

Predictive NO x emission monitoring on board a passenger ferry

NO x emissions from a medium speed diesel engine on board a servicing passenger ferry have been indirectly measured using a predictive emission monitoring system (PEMS) over a 1-yr period. Conventional NO x measurements were carried out with a continuous emission monitoring system (CEMS) at the start of the study to provide historical data for the empirical PEMS function. On three other occasions during the year the CEMS was also used to verify the PEMS and follow any changes in emission signature of the engine. The PEMS consisted of monitoring exhaust O 2 concentrations (in situ electrochemical probe), engine load, combustion air temperature and humidity, and barometric pressure. Practical experiences with the PEMS equipment were positive and measurement data were transferred to a land-based office by using a modem data communication system. The initial PEMS function (PEMS1) gave systematic differences of 1.1–6.9% of the calibration domain (0–1725 ppm) and a relative accuracy of 6.7% when compared with CEMS for whole journeys and varying load situations. Further improvements on the performance could be obtained by updating this function. The calculated yearly emission for a total engine running time of 4618 h was 316 t NO x ±38 t and the average NO x emission corrected for ambient conditions 14.3 g kWh corr −1. The exhaust profile of the engine in terms of NO x , CO and CO 2 emissions as determined by CEMS was similar for most of the year. Towards the end of the study period, a significantly lower NO x emission was detected which was probably caused by replacement of fuel injector nozzles. The study suggests that PEMS can be a viable option for continuous, long-term NO x measurements on board ships.