PH neutralization reaction process plays a crucial role in Waste Water Treatment Process (WWTP). Traditional PID Proportion Integral Differential, (or even advanced PID control) algorithms have poor performance on WWTP due to the strong non-linearity, large time lag, and large inertia characteristics of pH neutralization. Therefore, finding a superior control method to maintain the pH value of wastewater within the normal range will greatly help to improve the efficiency and effectiveness of wastewater treatment. The chemical reaction mechanism of pH neutralization reaction process is first analyzed, and a mechanistic model of pH neutralization reaction process is developed based on the reaction of ions during acid-alkali neutralization and the electric balance equation. Then, combining the characteristics of generalized predictive control and Model-Free Adaptive Control (MFAC), a Model-Free Adaptive Predictive Control (MFAPC) method based on compact format dynamic linearization is introduced. An Improved Model Free Adaptive PI Predictive Control algorithm (IMFAPC) with proportional (P) and integral (I) algorithms is proposed to further improve the control performance. IMFAPC is proposed on the basis of MFAPC, combining the advantages of generalized predictive control, introducing a PI module consisting of error and error sum, and predicting the PI module, making it possible to produce more accurate constraints on the control inputs, avoiding increasing errors, and improving the control effect of delayed systems at the same time. pH neutralization process simulation experimental results show that compared with the ordinary Model-Free Adaptive Control (MFAC) and MFAPC, the IMFAPC control algorithms has the best performance in terms of accuracy, overshoot, and the robustness.

As the global third-largest stationary source of carbon emissions, petroleum refineries have attracted much attention. Many investigations and methodologies have been used for the quantification of carbon emissions of refineries at the industry or enterprise scale. The granularity of current carbon emissions data impairs the reliability of precise mitigation, so analysis and identification of influencing factors for carbon emissions at a more micro-level, such as unit level, is essential. In this paper, four typical units, including fluid catalytic cracking, Continuous Catalytic Reforming, delayed coking, and hydrogen production, were chosen as objects. A typical 5-million-ton scale Chinese petroleum refinery was selected as an investigating object. The Redundancy analysis and multiple regression analysis were utilized to explore the relationship between the process parameters and carbon emissions. Three types of influencing factors include reaction conditions, processing scale, and materials property. The most important mitigation of carbon emission, in this case, can be summarized as measures of improving energy efficiency via optimizing equipment parameters or prompting mass efficiency by upgrading the scale for material and energy flow.

Emerging pollutants (EPs) associated with the petroleum industry present considerable challenges to environmental management and sustainable development. To support sustainable development and improve the control of EPs in the petroleum industry, this review systematically examines the functional uses of EPs as chemical additives across the entire petroleum supply chain—from extraction and transportation to refining and product blending. It also summarizes the environmental emissions, health impacts, mitigation strategies, and current regulatory frameworks of EPs. In addition, some challenges have been found, namely unclear data on EPs in chemical additives, insufficient attention to high-risk areas, undefined health risks of mixing EPs, lack of green assessment of alternative technologies, and regional policy disparities, which collectively hinder the effective prevention and management ofEPs. In response, we propose future perspectives including enhanced screening and substitution of high-EP-risk additives, development of source-specific fingerprinting techniques, expanded monitoring of mixed contaminants and understudied regions, accelerated deployment of green technologies, and strengthened global cooperation under sustainability-oriented governance frameworks. This study underscores the necessity of integrated, science-based approaches to align petroleum industry practices with global sustainability goals. This review underscores the critical need for a proactive and integrated approach toward the sustainable development of the petroleum industry through the control of and reduction in EPs.

The top–down emission rate retrieval algorithm (TERRA) method for calculating the net flux out of a box has been employed by other researchers to assess large sources of methane release. This usually requires a manned aircraft drone with powerful performance to fly over the boundary layer. Few studies have focused on low-altitude box sampling mass balance methods for small sources of methane release, such as at maximum flight altitudes of less than 100 m. The accuracy and sources of uncertainty in such a method still need to be determined as they differ from the conditions of large sources. Nineteen flights were conducted to detect methane emissions from Chinese oil field well sites using a measurement system consisting of a quadcopter and methane, wind speed, wind direction, air pressure, and temperature sensors. The accuracy and uncertainty of the method are discussed. The average absolute relative error of the measurement is 18.5%, with an average uncertainty of 55.75%. The uncertainty is mainly caused by the wind speed and direction, and the background CH₄ concentration. The main paths to reduce uncertainty and improve accuracy for low-altitude box sampling include subtracting the background concentration during flux retrieval, enhancing the accuracy of methane measurements, selecting a period of downwind dominant or wind direction change of less than 30 degrees, and ensuring a maximum flight height greater than 50 m with a horizontal distance from the pollution source center of less than 75 m. The results show that TERRA-based low-altitude box sampling is suitable for quantifying methane release rates from small sources.

This study presents a novel top-down approach to quantify diffuse methane (CH₄) emissions at oil and gas well sites. It uses an unmanned aerial vehicle (UAV) equipped with a scanning–sampling tunable diode laser absorption spectroscopy (TDLAS) CH₄ measurement instrument. By integrating the top-down emission rate retrieval algorithm (TERRA) and adopting concentric circular sampling, the method aims to overcome the limitations of traditional ground-based measurements. The UAV system was deployed at 11 oil and gas sites in the Changqing Oilfield. The results show that the average CH₄ emission rate detected by the UAV is 1.425 kg/h (excluding non-detected samples), which is larger than the 1.061 kg/h obtained from ground-based onsite direct measurement. This discrepancy may be because the UAV’s scanning–sampling capability can cover a larger area, capturing scattered or hidden diffuse emission sources that might be missed by ground-based onsite direct measurement. The study demonstrates that the UAV-based approach with a scanning–sampling TDLAS CH₄ measurement instrument, integrated with the TERRA and concentric circular sampling, is effective in capturing diffuse CH₄ emissions at oil and gas well sites, providing a viable method for large-scale and efficient monitoring of such emissions. This approach could provide an effective pathway for large-scale, efficient, and cost-effective monitoring of methane emissions.

Nonylphenols (NPs), widely used as emulsifiers in petroleum production and refining, are compounds of environmental concern, with endocrine-disrupting effects. They can be released during oil extraction and processing, carried into petroleum products, and subsequently emitted during downstream applications such as combustion. Despite these potential pathways, information on their occurrence in petroleum streams remains limited, partly due to the lack of reliable methods for measuring NPs in complex petroleum matrices. In this study, we developed an analytical method combining normal-phase chromatography (NPC), solid-phase extraction (SPE), and liquid chromatography–Orbitrap high-resolution mass spectrometry (LC–Orbitrap-HRMS) for NP determination in crude oils and petroleum products. NPC was performed using alumina (5% water deactivation) as the stationary phase. The column was eluted sequentially with n-hexane, n-hexane/dichloromethane (4:1 and 1:1, v/v), dichloromethane, and dichloromethane/methanol (2:1, v/v). The first three fractions were discarded, and the remaining two fractions were combined and further purified using a C₁₈ SPE cartridge to analysis. The method showed high recovery (82.8 ± 2.6%) and a low detection limit (1.0 ng/g) in crude oil. Application revealed widespread occurrence of NPs, with concentrations up to 784.4 ng/g in crude oils and up to 439.1 ng/g in refined fuels, indicating that these compounds can persist through refining and may be released during downstream use. These results demonstrate that the method is suitable for the routine monitoring of NPs in petroleum-related samples and provide a practical tool for supporting sustainable refining practices and improved environmental management in the upstream oil and gas sector.