Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

Due to the significant impacts of CO₂-oil miscibility, reservoir heterogeneity, and injection pattern on oil production, a fascinating experimental investigation is provided. The outcomes express that CO₂ consumption in homogeneous cores are lower than heterogeneous cores. A small amount of permeability heterogeneity as one of the indications of heterogeneity, can highly effect on the performance of miscible and immiscible injections. On the miscibility viewpoint, multi-contact miscible injections have better performance than immiscible injections in range of 7.2% − 16.4%. Also, the injection patterns comparisons express that best choice for pattern injection is water alternating gas injection.

Carbon geo-sequestration (CGS), as a well-known procedure, is employed to reduce/store greenhouse gases. Wettability behavior is one of the important parameters in the geological CO₂ sequestration process. Few models have been reported for characterizing the contact angle of the brine/CO₂/mineral system at different environmental conditions. In this study, a smart machine learning model, namely Gene Expression Programming (GEP), was implemented to model the wettability behavior in a ternary system of CO₂, brine, and mineral under different operating conditions, including salinity, pressure, and temperature. The presented models provided an accurate estimation for the receding, static, and advancing contact angles of brine/CO₂ on various minerals, such as calcite, feldspar, mica, and quartz. A total of 630 experimental data points were utilized for establishing the correlations. Both statistical evaluation and graphical analyses were performed to show the reliability and performance of the developed models. The results showed that the implemented GEP model accurately predicted the wettability behavior under various operating conditions and a few data points were detected as probably doubtful. The average absolute percent relative error (AAPRE) of the models proposed for calcite, feldspar, mica, and quartz were obtained as 5.66%, 1.56%, 14.44%, and 13.93%, respectively, which confirm the accurate performance of the GEP algorithm. Finally, the investigation of sensitivity analysis indicated that salinity and pressure had the utmost influence on contact angles of brine/CO₂ on a range of different minerals. In addition, the effect of the accurate estimation of wettability on CO₂ column height for CO₂ sequestration was illustrated. According to the impact of wettability on the residual and structural trapping mechanisms during the geo-sequestration of the carbon process, the outcomes of the GEP model can be beneficial for the precise prediction of the capacity of these mechanisms.

Hydraulic fracturing creates a high conductivity channel within a large area of formation and bypasses any damage that may exist in the near wellbore region. Moreover, it has been one of the major well stimulation techniques to increase well production. In order to model and study the feasibility of implementing this technique in one of the Iranian Southwest oil reservoirs, an extensive literature survey was carried out. One-dimensional mechanical earth model was constructed. Stable mud window was determined and types of failures were diagnosed. Consequently, on these bases, geomechanical feasibility was confirmed and hydraulic fracturing treatment for the case study was designed. Different scenarios were defined and sensitivity analysis was conducted together with the confirmation of economic feasibility. Furthermore, channel fracturing technique was employed and it was found that using this technique can be more suitable and beneficial than conventional fracturing method.