Basic Oxygen Furnace Steelmaking Research Articles

A Strategy for Partial Replacement of Lime by Limestone and Its Impact on Basic Oxygen Furnace Steelmaking

Production of steel in a sustainable manner is gaining importance day by day. Optimizing fluxes in basic oxygen furnace (BOF) steelmaking, such as partial replacement of lime with limestone, can be a step toward this. The heat available in BOF can decompose the limestone which partially avoids the need for calcination in lime kiln. However, the size, quantity, and time of limestone addition in BOF are important parameters as they influence the decomposition behavior of limestone. The associated problems of slopping, presence of uncalcined limestone, fines generation, etc. are also influenced by these parameters. In the present study, laboratory experiments are conducted by changing the size, temperature, and time of exposure which help in understanding high‐temperature behavior of limestone. With these inputs, along with theoretical calculations, a trial is conducted in a 160 t BOF by limestone addition and replacing equivalent amount of lime. The study focusses on the benefits of limestone addition in BOF along with other aspects, that is, impact on blow profiles, slag condition, lining wear, etc. Characterization of slags help in explaining the improvement observed during this trial. The article presents the findings of laboratory studies and limestone addition trials and uniquely highlights its overall impact on BOF steelmaking.

Pressurized Chemical Looping for Direct Reduced Iron Production: Economics of Carbon Neutral Process Configurations

The replacement of the blast furnace—basic oxygen furnace (BF-BOF) steelmaking route with the direct reduced iron—electric arc furnace (DRI-EAF) route reduces the direct CO2 emissions from steelmaking by up to 68%; however, the DRI shaft furnace is one of the largest remaining point source emitters in steelmaking. The capital and operating expenses of two potential nearly carbon-neutral DRI process configurations were investigated as a modification to a standard Midrex DRI facility. First, amine-based post-combustion capture with a 95% capture rate was considered as the benchmark, as it is currently commercially available. A second, novel configuration integrated the Midrex process with pressurized chemical looping—direct reduced iron (PCL-DRI) production. The capital expenditures were 71% and 28% higher than the standard Midrex process for a Midrex + amine capture plant, and a PCL-DRI plant, respectively. There was an incremental variable operating cost of USD 103 and USD 44 per tonne of CO2 for DRI production using amine capture and PCL-DRI, respectively. The amine capture configuration is most sensitive to the cost of steam generation, while PCL-DRI is more sensitive to the cost of electricity and the makeup oxygen carrier. An iron-based natural ore is recommended for PCL-DRI due to the low cost and availability. Based on the lower costs compared to amine-based post-combustion capture, PCL-DRI is an attractive means of eliminating CO2 emissions from DRI production.

Prediction of end-point phosphorus content of molten steel in BOF with machine learning models

The main task of basic oxygen furnace (BOF) steelmaking is dephosphorization, thus the prediction and control of End-point phosphorus content of molten steel is of great significance. Four machine learning regression models (Lasso, Random Forest, Xgboost, and Neural Network) were established to predict end-point phosphorus content of molten steel in BOF according to raw and auxiliary material data, process parameters, and data of production quality. The prediction effect of four models were further compared, and their prediction results were interpreted via model?s interpretability and Permutation Importance Method. Results showed that compared with linear regression and Neural Network regression model, two kinds of ensemble tree model had higher prediction accuracy, better stability in small data sets, and lower requirements on data pre-processing. The influencing factors of end-point phosphorus (P) content in BOF were ranked by importance as: Tapping temperature > Turning down times > Steel scrap amount > Operation habits of different work groups > Blowing oxygen amount > Sulfur and Phosphorus content of molten iron > Addition amount of lime, limestone, and light-burned dolomite in slagging agents > Slag-splashing amount.

BOF steelmaking endpoint carbon content and temperature soft sensor based on supervised dual-branch DBN

Accurate prediction of endpoint carbon content and temperature is critical in the basic oxygen furnace (BOF) steelmaking process. Although deep learning soft sensor approaches have the capacity to extract abstract features from high-dimensional nonlinear steelmaking data, they confront the challenge of a low correlation between acquired features and labels. This work presents a BOF steelmaking soft sensor model based on supervised dual-branch deep belief network (SD-DBN) to address this issue. The SD-DBN model incorporates label information into the feature extraction process and fuses crucial feature information to complete the feature extraction in order to extract features that are closely connected to the target variables. First, the supervised Restricted Boltzmann Machine (RBM) is improved by using a pruning strategy to extract features that are highly correlated with quality information, and then the autocorrelation key feature extraction module is spliced and fused to form a dual-branch feature extraction module to improve key information extraction. Second, stacking the supervised dual-branch RBM modules to build a deep feature extraction network enhances the deep extraction capabilities of data features. This deep network stacking not only increases the impact of essential target data in hierarchical training, but it also acquires characteristics associated with the target variables.

Vibration and Audio Measurements in the Monitoring of Basic Oxygen Furnace Steelmaking

A basic oxygen furnace (BOF) is the main unit process for refining carbon steel. The aim of this work was to study the use of vibration and audio signal measurements to monitor, predict, and control the BOF process. Vibration and audio data from nearly 300 blows were collected and analyzed together with process variables. We could confirm high correlations between some of the process variables and vibration and audio measurements. Median filtered low-frequency (3–20 Hz) audio as well as X- and Z-direction acceleration root mean square (RMS) time series correlate with the off-gas temperature, although this is much more significant for the audio data. For Y-direction measurements (the upward direction) the correlation is negligible. The low-frequency audio and vibration data are likely related to the rate of decarburization. Median filtered mid-frequency (100–1000 Hz) audio as well as X-, Y-, and Z-direction acceleration RMS time series correlate with the lance height measurement during the interval 20–600 seconds from the beginning of oxygen blow. For the audio data, the correlation was high even without median filtering. We suggest that audio and vibration activity in this frequency range is possibly related to the formation of the metal–slag–gas foam and maybe even to slopping.

Artificial Neural Network Model for Temperature Prediction and Regulation during Molten Steel Transportation Process

With the continuous optimization of the steel production process and the increasing emergence of smelting methods, it has become difficult to monitor and control the production process using the traditional steel management model. The regulation of steel smelting processes by means of machine learning has become a hot research topic in recent years. In this study, through the data mining and correlation analysis of the main equipment and processes involved in steel transfer, a network algorithm was optimized to solve the problems of standard back propagation (BP) networks, and a steel temperature forecasting model based on improved back propagation (BP) neural networks was established for basic oxygen furnace (BOF) steelmaking, ladle furnace (LF) refining, and Ruhrstahl–Heraeus (RH) refining. The main factors influencing steel temperature were selected through theoretical analysis and heat balance principles; the production data were analyzed; and the neural network was trained and tested using large amounts of field data to predict the end-point steel temperature of basic oxygen furnace (BOF) steelmaking, ladle furnace (LF) refining, and Ruhrstahl–Heraeus (RH) refining. The prediction model was applied to predict the degree of influence of different operating parameters on steel temperature. A comparison of the prediction results with the production data shows that the prediction system has good prediction accuracy, with a hit rate of over 90% for steel temperature deviations within 20 °C. Compared with the traditional steel temperature management model, the prediction system in this paper has higher management efficiency and a faster response time and is more practical and generalizable in the thermal management of steel.

Thermodynamic modelling of steelmaking slag to reduce refractory consumption during CONARC® process

ABSTRACT CONARC® steelmaking process which combines the basic oxygen and electric arc furnace steelmaking, requires precise operation and control of slag chemistry within an optimum range to reduce the refractory wear. The target slag should be MgO saturated, containing solid particles of magnesium wustite which ensures good foamy slag and causes less erosion of MgO-C refractory. In this present study, FactSage™ software was utilized to study and visualize the MgO saturation of slag through MgO-SiO2-FeO-CaO-Al2O3-MnO and CaO-SiO2-FeO-MgO-Al2O3-MnO systems. The ternary phase diagrams revealed, the slag was MgO-under saturated based on the current pattern of calcined lime and dolomite addition during the process. Based on the findings of this study, the addition pattern were altered to bring the slag regime within the MgO saturation level with good slag basicity. This change showed significant improvement in reducing the erosion rate of MgO-C lining in furnace and thereby reducing the specific gunning mass requirement.

Soft sensor method of multimode BOF steelmaking endpoint carbon content and temperature based on vMF-WSAE dynamic deep learning

Abstract The difficulty of endpoint determination in basic oxygen furnace (BOF) steelmaking lies in achieving accurate real-time measurements of carbon content and temperature. For the characteristics of serious nonlinearity between process data, deep learning can perform excellent nonlinear feature representation for complex structural data. However, there is a process drift phenomenon in BOF steelmaking, and the existing deep learning-based soft sensor models cannot adapt to changes in the characteristics of samples, which may lead to their performance degradation. To deal with this problem, considering the characteristics of multimode distribution of process data, an adaptive updating deep learning model based on von-Mises Fisher (vMF) mixture model and weighted stacked autoencoder is proposed. First, the stacked autoencoder (SAE) and vMF mixture model are constructed for complex structural data, which can initially establish nonlinear mapping relationships and division of different distributions. Second, for each query sample, the basic SAE network will perform online adaptive fine-tuning according to its data with the same distribution to achieve dynamic updating. Moreover, each sample is assigned a weight according to its similarity with the query sample. Through the designed weighted loss function, the updated deep network will better match the working conditions of the query sample. Experimental studies with numerical examples and actual BOF steelmaking process data are provided to demonstrate the effectiveness of the proposed method.

End‐Point Dynamic Control Model for 260 Tons BOF Steelmaking Based on PWWTSVR and LWOA

The control of end‐point carbon content and temperature is very important for basic oxygen furnace (BOF) steelmaking. Herein, to improve the control precision of the BOF end‐point, a new dynamic control model of the BOF end‐point is proposed. First, based on the samples of low‐carbon steel collected from the steel plant, a prediction model of BOF end point is established by using projection wavelet weighted twin support vector regression (PWWTSVR). It is indicated in the simulation results that the error bound of carbon content and temperature are 0.005 wt% and 10 °C, respectively, and the hit rates of the prediction models are 92% and 90%. Moreover, a double hit rate of 84% is higher than the other five prediction models. Based on the PWWTSVR prediction model, a Lévy‐flying whale optimization algorithm (LWOA) is used to optimize the objective function to establish the control model. The control model shows that the mean absolute error of the end‐blow oxygen volume calculated by the model is 75.0201 Nm3, which is smaller by comparing the other two control models. The proposed control model can provide efficient guidance for on‐site smelting personnel.

Numerical Simulation of Motion and Distribution of Powder Particles Injected from a Nozzles-Twisted Oxygen Lance in BOF Steelmaking

The pulverized lime/limestone injection by top oxygen blowing lance during the basic oxygen furnace (BOF) process has gained much interest in recent years due to its advantages in helping slag formation and consequently in promoting refining reactions such as dephosphorization. In this pneumatic process, understanding the motion behavior and distribution of the powder particles in the furnace is of importance for regulating and designing this refining system reliably and efficiently. In this study, limestone powder top blowing through a novel nozzles-twisted oxygen lance during a BOF process is proposed and the process is simulated by establishing a multi-fluid flow model. The coupled fluid flow of gaseous oxygen and liquid steel is predicted by the volume of fluid (VOF) method, and the motion of the limestone particles is tracked by the discrete phase model (DPM). The results show that the powder injection has little effect on cavity depth of the oxygen-powder mixture jets of the nozzles-twisted lance, but decreases cavity width. During the blowing process, most of the powder particles gather around hot spots while the rest are taken out of the furnace by the reflecting oxygen stream or penetrate into the molten bath. The generated swirling flow of the nozzles-twisted oxygen lance enables a decrease in the amount of the powder particles carried by the reflecting stream and going into the molten bath, through changing the motion paths of the powder particles. As a result, the concentration distribution of the powder particles in the molten bath varies. It could be suggested that for the limestone powder injection the preferred nozzle twist angle of the oxygen lance is 10° due to the favorable conditions for dephosphorization.

A Novel Operation Optimization Method Based on Mechanism Analytics for the Quality of Molten Steel in the BOF Steelmaking Process

This paper investigates a dynamic analytics and operation optimization method for the basic oxygen furnace (BOF) steelmaking process, which is a multi-stage process. First, based on reaction kinetics, fluid dynamics and conservation of mass, that a novel discrete-time nonlinear system with process disturbance, time delay and the constrained operation input is established characterizes the dynamics of the quality (the carbon content and the temperature) of molten steel in the BOF steelmaking process. It is difficult and complicated to realize analytics and operation optimization for the BOF steelmaking process by using the established nonlinear system directly, so a surrogate-model-based discrete-time switched system with time delay and actuator saturation is given to describe the multi-stage BOF steelmaking process. Then, sufficient conditions are derived under which the stability is guaranteed and the tracking performance is achieved. The parameters of analytics-based operation optimization algorithm can be obtained by solving linear matrix inequality problems (LMIPs). Finally, the effectiveness of the proposed method is verified by a BOF steelmaking numerical experiment example. Note to Practitioners—This paper deals with the problem of dynamic analytics and operation optimization arising from the BOF steelmaking process. Based on the real-time estimation of the carbon content and the temperature of molten steel, the plan for the operation of oxygen lance and the addition of auxiliary materials is given to produce the qualified molten steel. This paper establishes a system model based on the mechanism, and the model parameters are derived from papers verified by a large amount of data to ensure the correctness of the model. In addition, actuator saturation is applied to describe the phenomenon of restricted operation variables. Through a surrogate model approximation and theoretical analysis, the parameters of dynamic analytics and operation optimization algorithm can be obtained by solving LMIPs, which is a convex optimization problem that is easy to solve. The numerical experiment results show that the proposed method can give operators some desired references.

A Metallurgical Dynamics-Based Method for Production State Characterization and End-Point Time Prediction of Basic Oxygen Furnace Steelmaking

Basic Oxygen Furnace (BOF) steelmaking is an important way for steel production. Correctly recognizing different blowing periods and abnormal refining states is significant to ensure normal production process, while accurately predicting the end-point time helps to increase the first-time qualification rate of molten steel. Since the decarburization products CO and CO2 are the main compositions of off-gas, information of off-gas is explored for BOF steelmaking control. However, the problem is that most of the existing research directly gave the proportions of CO and CO2 as model input but barely considered the variation information of off-gas to describe the production state. At the same time, the off-gas information can be expected to recognize the last blowing period and predict the end-point time earlier than the existing methods that are based on sub-lance or furnace flame image, but little literature makes an attempt. Therefore, this work proposes a new method based on functional data analysis (FDA) and phase plane (PP), defined as FDA-PP, to describe and predict the BOF steelmaking process from the metallurgical dynamics viewpoint. This method extracts the total proportion of CO and CO2 and its first-order derivative as dynamics features of steelmaking process via FDA, which indicate the reaction velocity and acceleration of decarburization reaction, and describes the evolution of dynamics features via PP. Then, the FDA-PP method extracts the features of phase trajectories for production state recognition and end-point time prediction. Experiments on a real production dataset demonstrate that the FDA-PP method has higher production state recognition accuracy than the classical phase space, SVM, and BP methods, which is 87.78% for blowing periods of normal batches, 90.94% for splashing anomaly, and 81.29% for drying anomaly, respectively. At the same time, the FDA-PP method decreases the mean relative prediction error (MRE) of the end-point time prediction for abnormal batches by about 10% compared with the SVM and BP methods.

Comparison of the Prediction of BOF End‐Point Phosphorus Content Among Machine Learning Models and Metallurgical Mechanism Model

The five machine learning models (MLM) of ridge regression, gradient boosting regression (GBR), support vector regression, random forest regression (RFR), convolutional neural network, and a metallurgical mechanism model (MMM) are compared in predicting the end‐point P content in the basic oxygen furnace steelmaking process. The prediction accuracy of MMM is much lower than those of five MLM. The GBR and RFR models have the best performance, with the correlation coefficient values of 0.599 and 0.608, respectively. The smallest mean absolute relative error value of 0.155 and the root mean square error value of 0.00319 are obtained with GBR and RFR, respectively. The values of correlation coefficient after data distribution optimization for all MLM are increased two times higher than before. The second blowing time, lime weight, and oxygen consumption amount are evaluated to have the greatest impacts on the end‐point P content. The end‐point P content decreases with decreasing the second blowing time and with increasing the lime weight and the oxygen consumption amount. The GBR and RFR models are optimized by removing the variables with little impacts on the end‐point P content. The highest prediction accuracy is obtained when 14 variables are remained.

Just-in-time updating soft sensor model of endpoint carbon content and temperature in BOF steelmaking based on deep residual supervised autoencoder

The key of Basic oxygen furnace (BOF) endpoint control is to achieve accurate forecast of the endpoint carbon content and temperature. Due to the highly nonlinear and complex distribution characteristics of process data, the predictions of these two indicators are inaccurate. Although deep learning can extract abstract features of complex data, existing methods are pre-trained in an unsupervised manner. For soft sensor applications, the key is to highlight important information and extract features related to the target variables for prediction. Thus, this paper proposes a feature extraction model based on deep residual supervised autoencoder (DRSupAE). By incorporating important information in the feature of previous hidden layers into deeper feature learning in the form of residual connection, and each residual supervised autoencoder (RSupAE) utilizes the target variables to guide feature extraction. In this way, the stacking of new deep networks not only enhances the impact of important target-related information in layer-wise training, but also captures features related to target variables. Moreover, for the time-varying phenomenon of the BOF steelmaking process, a strategy of just-in-time updating regression network (JITRN) is proposed, which is used to solve the problem that the static regression model cannot adapt to the frequent changes of sample characteristics and causes the prediction performance to decline. An experimental study on BOF steelmaking process data is provided to demonstrate the effectiveness of the proposed method.

Hybrid Model for Predicting Oxygen Consumption in BOF Steelmaking Process Based on Cluster Analysis

The amount of oxygen blown into the converter directly affects the productivity of the converter and the quality of the molten steel, so it is crucial to accurately predict oxygen consumption. Herein, a hybrid model for predicting oxygen consumption in basic oxygen furnace steelmaking process is established. The oxygen consumption of converter is divided into two types: the determined oxygen consumption and the estimated oxygen consumption. The determined oxygen consumption is calculated by mechanism model. And the estimated oxygen consumption is calculated by statistical model. The method of cluster analysis is also introduced into the model. After clustering the data, a statistical model for predicting the estimated oxygen consumption in each subcategory is established, which further improves the prediction accuracy of the model. After dividing the original data into three categories, the hit rate of the hybrid model is 93.3%, when the relative errors of the model are restricted within 5%. As a result, the hybrid model is fully capable of predicting oxygen consumption in converters.

Investigations on the Interaction Behavior between Direct Reduced Iron and Various Melts.

Since the European Union defined ambitious CO2 emission targets, low-carbon-emission alternatives to the widespread integrated blast furnace (BF)—basic oxygen furnace (BOF) steelmaking strategy—are demanded. Direct reduction (DR) with natural gas as the reducing agent, already an industrially applied technology, is such an alternative. Consequently, the melting behavior of its intermediate product, i.e., direct reduced iron (DRI), in either an electric arc furnace (EAF) or a submerged arc furnace (SAF), is of great interest. Based on the conditions in these aggregates, a test series to experimentally simulate the first few seconds after charging DRI was defined. DRI samples with different carbon contents and hot briquetted iron (HBI) were immersed in high- and low-carbon melts as well as high- and low-iron oxide slags. The reacted samples were quenched in liquid nitrogen. The specimens were qualitatively evaluated by investigating their surfaces and cross sections. The dissolution of carbon-free DRI progressed relatively slowly and was driven by heat transfer. However, carbon, present either in the DRI sample or in the melt, not only accelerated the dissolution process, but also reacted with residual iron oxide in the pellet or the slag.

Prediction of oxygen-blowing volume in BOF steelmaking process based on BP neural network and incremental learning

Abstract In view of the characteristics of dynamic basic oxygen furnace (BOF) steelmaking process, prediction models based on backpropagation neural network and incremental learning (BPNN-IL) are proposed for total blow oxygen volume and second blow oxygen volume. The incremental learning is to adjust weights and thresholds of the BPNN according to the difference between the predicted value and actual value of each heat and to adapt to the change in furnace conditions. The combined BPNN-IL models are trained and tested by actual production data, and are further compared with multiple linear regression models and BPNN models. The results show that whether it is total blow oxygen volume or second blow oxygen volume, the BPNN-IL models could provide the most accurate prediction and the introduction of an incremental learning method could further improve the predictive accuracy. So the BPNN-IL method is effective in predicting the oxygen-blowing volume in the BOF steelmaking process.

Numerical simulation on the melting kinetics of steel scrap in iron-carbon bath

Steel scrap melting kinetics is a key factor that affects the scrap ratio and temperature trajectory of Basic Oxygen Furnace steelmaking process and the productivity and energy utilization of Electric Arc Furnace steelmaking process. In this paper, the interface between steel scrap and melt is analyzed and a novel theoretical model of steel scrap melting in iron-carbon bath is established. Moreover, the effects of bath temperature, bath carbon content, preheating temperature and characteristic length of steel scrap on the melting rate of steel scrap, mass transfer coefficient of carbon, heat transfer coefficient and interface carbon concentration were investigated. The results show that the ultimate steady melting rate increases from −2.04 × 10 −4 to −3.32 × 10 −3 m/s with the increases of bath temperature from 1793 K to 1873 K, the ultimate steady heat and mass transfer coefficient first increase and subsequently decrease, are in the range of 1.04 × 10 5 to 2.86 × 10 5 W/m 2 /K and 0 to 2.89 × 10 −4 m/s, respectively. As the bath carbon content increase from 1.0 wt% to 4.0 wt%, the ultimate steady melting rate increases from −8.85 × 10 −5 to −3.23 × 10 −4 m/s, the ultimate steady heat transfer coefficient first increases and subsequently decreases, is in the range of 7.59 × 10 4 to 1.01 × 10 5 W/m 2 /K. Furthermore, the ultimate steady mass transfer coefficient and the ultimate steady interface carbon content gradually decreases from 1.40 × 10 −4 to 5.21 × 10 −5 m/s and from 0.73 to 0.69 wt%, respectively. With the increase of preheating temperature and characteristic length of steel scrap, the ultimate steady melting rate, heat transfer coefficient between steel scrap and melt, mass transfer coefficient and interface carbon concentration remain almost constant, about −8.85 × 10 −5 m/s, 8.40 × 10 4 W/m 2 /K, 1.40 × 10 −4 m/s and 0.70 wt%, respectively.

70 Years of LD-Steelmaking—Quo Vadis?

Basic Oxygen Furnace (BOF) steelmaking is, worldwide, the most frequently applied process. According to the world steel organization statistical report, 2021, it saw a total production share of 73.2%, or 1371.2 million tons per year of the world steel production in 2020. The rest is produced in Electric Arc Furnace (EAF)-based steel mills (26.3%), and only a very few open-hearth and induction furnace-based steel mills. The BOF technology remains the leading technology applied based on its undoubted advantages in productivity and liquid steel composition control. The BOF technology started as the LD process 70 years ago, with the first heat applied in November 1952 in a steel mill in Linz, Austria. The name LD was formed from the first letters of the two sites with the first industrial scale plants, Linz and Donawitz, both in Austria. The history and development of the process have been honored in multiple anniversary publications over the last few decades. Nevertheless, the focus of the steel industry worldwide is significantly changing following a social and political trend and the requirement for fossil-free energy generation and industrial production to be in accordance with the world climate targets committed to in relation to the decades leading up to 2050. Iron and steel production is one of the major polluters of climate changing greenhouse gases; it must change to renewable primary energy sources and the use of climate-neutral reduction agents. Because it is very obvious that carbon, as the main component for steel strength properties, cannot be eliminated totally from the steel production process, the question arises of where a “zero carbon” approach can lead? This paper will review the ongoing success story of the LD-process, discuss the recent technology advancements, and give an outlook on the future role of the process in the steel industry.

Green LD (BOF) Steelmaking—Reduced CO2 Emissions via Increased Scrap Rate

The basic oxygen furnace (BOF) is the dominating primary steelmaking process. It is an autothermal process where hot metal and scrap are used as charging materials. The decarbonization and transformation of integrated BOF steelmaking will be the most important challenge in the coming years. Steel scrap is a charge material without new CO2 emissions, whose availability is expected to grow significantly and will play a key role in this decarbonization process. Several solutions have been developed by Primetals Technologies to provide additional energy for processing higher scrap rates in integrated BOF steelmaking. Such solutions include simple upgrade packages installed on existing converters such as process models for heat optimization, post-combustion, and scrap preheating lances. For higher scrap rates from 30% to 50%, a combination blowing converter and JET converter is required to provide sufficient mixing during scrap melting and the highest heat transfer from the increased post-combustion. Hybrid EAF–BOF operation and limitations regarding scrap quality also need to be considered for the transformation of steelmaking. Scrap sorting and processing can be a solution to reduce residual levels in crude steel for high scrap rates. Based on reference plant data, the CO2 reduction potential of the presented solution versus the effort and complexity of implementation is compared.