Carbonate reservoirs account for 60% of global reserves for oil, making them one of the most important types of sedimentary rock reservoirs for petroleum production. This study aimed to identify key production features that significantly impact oil production rates, enhancing reservoir management and optimizing production strategies. A comprehensive dataset is built from reserves and production history data of 377 onshore carbonate oilfields globally, encompassing features such as production, recovery rate, and recovery degree of the whole lifecycle of an oilfield. XGBoost classifier is trained by K-fold cross-validation and its hyperparameters are optimized by Optuna optimization framework. The results show that XGBoost has the best performance evaluated with metrics including accuracy, precision, recall, and F1 score comparing with decision tree, random forest, and support vector machine. Key production features are identified by analyzing the classification feature importance of XGB... [more]

In recent years, companies have employed numerous methods to lower expenses and enhance system efficiency in the oilfield. Energy consumption has constituted a significant portion of these expenses. This paper introduces a normalized consumption factor to effectively evaluate energy consumption in the oilfield. Statistical analysis has been conducted on nearly 45,000 wells from six fields in China. Critical factors such as lifting method, daily production, pump depth, gas−oil ratio (GOR), and well deviation angle were evaluated individually. Results revealed that higher production could lead to lower normalized consumption for beam pumps, progressive cavity pumps, and electric submersible pump systems, thus enhancing system efficiency. Additionally, a higher GOR might result in lower normalized consumption for the beam pump system, while the deviation angle of the well showed negligible impact on the normalized consumption factor. This manuscript offers a method to assess the impacts o... [more]

Longitudinal cracks are a common defect on the surface of continuous casting slabs, and cause increases in additional processing costs or long-time interruptions. The accurate identification of surface longitudinal cracks is helpful to ensure the casting process is adjusted in time, which significantly improves the quality of slabs. In this research, the typical temperature characteristics of thermocouples at the position of longitudinal cracks and their adjacent locations were extracted. The principal component analysis (PCA) method was used to reduce the dimensions of these characteristics to remove the redundant information. The particle swarm optimization (PSO) method was introduced to optimize the parameter. On this basis, a recognition model of surface longitudinal cracks was established, based on a particle swarm optimization−eXtreme gradient boosting (XGBOOST) model. Finally, this model was trained and tested using longitudinal crack and non-longitudinal crack samples and compa... [more]

Understanding subsurface natural fracture systems is crucial to characterize well production dynamics and long-term productivity potential. In addition, the placement of future wells can benefit from in-depth fracture network connectivity investigations, vastly improving new wells’ profitability and life cycles if they are placed in dense, well-connected natural fracture zones. In this study, a novel natural fracture calibration workflow is proposed. This workflow starts with the extraction of sector geology and a natural fracture model from the pre-built full-field model. Then, a cross wellbore discrete fracture network (CW-DFN) is created using a novel CW-DFN generation tool, based on image log data. An innovative fracture network identification tool is developed to detect the interconnected regional fracture network (IcFN) with CW-DFN. The non-intrusive embedded discrete fracture model (EDFM) is utilized to numerically incorporate the complex IcFN and CW-DFN in a reservoir simulatio... [more]

With the continuous improvement in production efficiency and quality of life, the requirements of electrical equipment for power quality are also increasing. Accurate detection of various power quality disturbances is an effective measure to improve power quality. However, in practical applications, the dataset is often contaminated by noise, and when the dataset is not sufficient, the computational complexity is too high. Similarly, in the recognition process of artificial neural networks, the local optimum often occurs, which ultimately leads to low recognition accuracy for the trained model. Therefore, this article proposes a power quality disturbance classification strategy based on the variational mode decomposition (VMD) and improved support vector machine (SVM) algorithms. Firstly, the VMD algorithm is used for preprocessing disturbance denoising. Next, based on the analysis of typical fault characteristics, a multi-SVM model is used for disturbance classification identification... [more]

Predicting carbon emissions is important in various sectors, including environmental management, economic planning, and energy policy. Traditional forecasting models typically require extensive training data to achieve high accuracy. However, carbon emission data are usually available on an annual basis, which is insufficient for effectively training conventional forecasting models. To address this challenge, this paper introduces an innovative carbon emissions prediction model that integrates Fibonacci attenuation particle swarm optimization (FAPSO) with the gated recurrent unit (GRU). The FAPSO algorithm is used to optimize the hyperparameters of the GRU, thereby alleviating the decline in prediction accuracy that conventional recurrent neural networks often face when dealing with limited training data. To evaluate the effectiveness of the FAPSO-GRU model, we tested it using carbon emission data from Hainan Province. Compared to the conventional GRU model, the FAPSO-GRU model achieve... [more]

The development of renewable energy and the construction of a comprehensive energy system with multiple complementary energy sources have gradually become the main direction of China’s energy development. As the penetration rate of renewable energy increases, the intermittent and fluctuating output of wind and solar power has a more significant impact on the system. This article conducts research on the optimization configuration of integrated energy system (IES) considering photovoltaic output uncertainty under a ladder carbon trading mechanism. Firstly, a two-stage distributed robust optimization (DRO) configuration model for integrated energy system is established. In detail, a deterministic model aimed at minimizing investment costs is given in the first stage and an uncertainty model aimed at minimizing operating costs in the probability distribution of the worst scenario is built in the second stage. Then, a data-driven distributed robust optimization method is adopted to deal wi... [more]

Carbon dioxide (CO2) has been recognized as one of the potential working fluids to operate power generation cycles, either in supercritical or transcritical configuration. However, a small concentration of some of the additives to CO2 have shown promising improvements in the overall performance of the cycle. The current study is motivated by the newly proposed additive silicon tetrachloride (SiCl4), and so we perform a detailed investigation of SiCl4 along with a few well-known additives to CO2-based binary mixtures as a working fluid in transcritical organic Rankine cycle setup with internal heat regeneration. The additives selected for the study are pentane, cyclopentane, cyclohexane, and silicon tetrachloride (SiCl4). A comprehensive study on the energy and exergy performance of the cycle for warm regions is conducted at a turbine inlet temperature of 250 °C. The performance of the heat recovery unit is also assessed to highlight its importance in comparison to a simple configuratio... [more]

This study develops a one-dimensional analytical solution for contaminant advection, diffusion and adsorption through a soil−bentonite (SB)/geosynthetic clay liner (GCL)/SB−aquifer composite cutoff wall (CCW) system. The solution agrees well with an existing double-layer model. Adopting toluene as a representative contaminant, using the present solution, the analysis systematically investigates the impact of hydraulic gradient (i) and the hydraulic conductivities of GCL (kgcl) and SB (ksb). The results show the following: (1) Increasing i from 0.1 to 1 reduces the concentration breakthrough time (tcb) from 20 to 11 years and mass flux breakthrough time (tfb) from infinite to 11 years, indicating lower i significantly extend both tcb and tfb, which is crucial for optimizing CCW barrier performance; (2) lowering kgcl from 5.0 × 10−11 m/s to 1 × 10−12 m/s and reducing ksb from 1.0 × 10−9 m/s to 1.0 × 10−11 m/s, would increase the tcb by 36% and 100%, respectively. It demonstrates that red... [more]

This research delves into the impact of Twist Winglets−Cross-Section Twist Tape (TWs-CSTT) structures within heat exchangers on thermal performance. Utilizing Computational Fluid Dynamics (CFD) and machine learning methodologies, optimal geometrical parameters for the TWs-CSTT configuration were examined. The outcomes demonstrate that fluid undergoing a rotational motion within tubes featuring this structure leads to more effective secondary flows, intensified mixing, and improved thermal boundary layer disturbance. Moreover, by integrating machine learning with multi-objective optimization techniques, the performance of heat exchangers can be accurately predicted and optimized, facilitating enhanced heat exchanger design. Through the application of the multi-objective optimization algorithm Non-dominated Sorting Genetic Algorithm II (NSGA-II), the ideal configurations for TWs-CSTT were ascertained: L1 is the cross-sectional length of the Twisted Wings, L2 is the radius of the Central... [more]

This work investigates the inverse coupled radiation−conduction problem for estimating thermophysical parameters and source terms by an improved grey wolf optimization (GWO). The transient coupled radiation−conduction heat transfer problem in participating slab media is solved by the finite volume method. The radiative intensities on both boundaries are adopted as known measurement information in the inverse model. To overcome the disadvantages of the original GWO algorithm, an improved grey wolf algorithm (IGWO) is developed by introducing the weight strategy and nonlinear factors. Three benchmark functions are adopted to prove that the IGWO has a faster convergence speed and higher estimation accuracy than the original one. The IGWO is applied to inverse the thermophysical parameters and source terms based on the coupled radiation−conduction model; the results indicate that the IGWO is accurate and effective for estimating refractive index, absorption coefficient, and source terms.

The high- and low-pressure switching of the axial piston pump is realized by the structure of the valve plate, and the buffer-groove structure on the valve plate is very important to reduce the pressure shock and flow fluctuation. In order to optimize the structural parameters of the buffer tank of the piston pump, the influence of the triangular-groove structure on the outlet pressure−flow characteristics was analyzed, and the low-pulsation buffer-groove structure was designed. First, the relationship between the structure of the triangular buffer tank and the output pressure and flow characteristics of the pump was analyzed theoretically. The Computational Fluid Dynamics (CFD) method was used to calculate the pressure and flow characteristics of the whole pump flow field of the triangular-groove structure, and the influence of the buffer-groove structure parameters on the outlet flow pulsation characteristics was studied. The multi-objective optimization algorithm was used to optimiz... [more]

Deep peak shaving achieved through the integration of energy storage and thermal power units is a primary approach to enhance the peak shaving capability of a system. However, current research often tends to be overly optimistic in estimating the operational lifespan of energy storage and lacks clear quantification of the cost changes associated with system peak shaving. This study proposes an optimized operation model for the joint operation of thermal power and energy storage while considering the lifespan degradation of energy storage and the deep peak shaving of thermal power. This model measures the cost changes due to the participation of energy storage in thermal power unit peaking. It is able to reflect the value of economic externalities of energy storage in the power system and has a positive reference effect on energy storage investment planning and energy storage subsidy pricing. First, an energy storage lifespan degradation model based on equivalent cycle counts is constru... [more]

The energy hub provides a comprehensive solution uniting energy producers, consumers, and storage systems, thereby optimizing energy utilization efficiency. The single integrated energy system’s limitations restrict renewable absorption and resource allocation, while uncoordinated demand responses create load peaks, and global warming challenges sustainable multi-energy system operations. Therefore, our work aims to enhance multi-energy flexibility by coordinating various energy hubs within a hydrogen-based integrated system. This study focuses on a cost-effective, ecologically sound, and flexible tertiary hub (producer, prosumer, and consumer) with integrated demand response programs, demonstrating a 17.30% reduction in operation costs and a 13.14% decrease in emissions. Power-to-gas technology enhances coupling efficiency among gas turbines, boilers, heat pumps, and chillers. A mixed-integer nonlinear programming model using a GAMS BARON solver will achieve the optimal results of thi... [more]

Hyperparameter tuning is crucial in the development of machine learning models. This study introduces the nonlinear shrinking factor and the Cauchy mutation mechanism to improve the Dujiangyan Irrigation System Optimization (DISO), proposing the improved Dujiangyan Irrigation System Optimization algorithm (IDISO) for hyperparameter tuning in machine learning. The optimization capabilities and convergence performance of IDISO were validated on 87 CEC2017 benchmark functions of varying dimensions and nine real-world engineering problems, demonstrating that it significantly outperforms DISO in terms of convergence speed and accuracy, and ranks first in overall performance among the seventeen advanced metaheuristic algorithms being compared. To construct a robust and generalizable prediction model for hydrochar element characteristics, this study utilized IDISO and DISO algorithms to fine-tune the parameters of the XGBoost model. The experimental results show that the IDISO-XGBoost model a... [more]

The production of deep-coalbed methane (CBM) wells undergoes four stages sequentially: drainage depressurization, unstable gas production, stable gas production, and gas production decline. Upon entering the stable production stage, the recovery rate of deep CBM wells is constrained by bottom hole flowing pressure (BHFP). Reducing BHFP can further optimize CBM productivity, significantly increasing the production and recovery rate of CBM wells. This paper optimizes the deliquification process for deep CBM in the Linxing Block. By analyzing the production of deep CBM wells, an improved sucker rod pump deliquification process is proposed, and a method considering the flow in the tubing, annulus, and reservoir is established. Using the production data of Well GK-25D in the Linxing CBM field as an example, an optimized design of the improved rod pump deliquification process was undertaken, with design parameters including the depth of the sucker rod pump, the stroke length, and stroke rate... [more]

The refining industry is shifting from decarbonization to hydrogenation for processing heavy fractions to reduce pollution and improve efficiency. However, the carbon footprint of hydrogen production presents significant environmental challenges. This study couples refinery linear programming models with life cycle assessment to evaluate, from a long-term perspective, the role of low-carbon hydrogen in promoting sustainable and profitable hydrogenation refining practices. Eight hydrogen-production pathways were examined, including those based on fossil fuels and renewable energy, providing hydrogen for three representative refineries adopting hydrogenation, decarbonization, and co-processing routes. Learning curves were used to predict future hydrogen cost trends. Currently, hydrogenation refineries using fossil fuels benefit from significant cost advantages in hydrogen production, demonstrating optimal economic performance. However, in the long term, with increasing carbon taxes, hydr... [more]

The excessive discharge of nitrogen leads to water eutrophication. The partial nitritation and anammox (PN/A) process is a promising technology for biological nitrogen removal in wastewater treatment. However, applying it to mature landfill leachate (MLL) faces challenges, as the toxic substances (e.g., heavy metal) within MLL inhibit the activity of anammox bacteria. Therefore, most previous studies focused on diluted, pretreated, or chemically adjusted MLL. This study demonstrated at full scale that the two-stage PN/A process can treat raw MLL. Initially, the operational issue of sludge floatation resulted in rapid biomass loss with overflow discharging, which selectively suppresses nitrite-oxidizing bacteria (NOB), promoting the achievement of nitrite accumulation. After that, the NOB suppression was self-sustained by the high in situ free ammonia concentration, i.e., 26.2 ± 15.9 mg N/L. In the subsequent anammox tank, nitrogen removal primarily occurred via the anammox process, com... [more]

Flexibility is a critical feature of any industrial system as it tells us about the range of conditions under which the system can effectively and safely operate. It is becoming increasingly important as we face greater volatilities in market conditions, diverse customer needs, more stringent safety and environmental regulations, the growing use of resources with varying availability such as renewable energy, and an increased likelihood of disruptions caused by, for example, extreme weather... (ABSTRACT ABBREVIATED)

Managing oil and gas produced water, characterized by hypersalinity and large volumes, presents significant challenges. This paper introduces an advanced optimization framework, PARETO, which offers a novel approach to strategic water management, emphasizing produced water (PW) treatment, quality tracking, quantification of emissions, and environmental justice. This work presents a case study showcasing different produced water management challenges. The PARETO framework demonstrated its effectiveness in optimizing water management strategies in line with environmental sustainability and regulatory compliance.

Critical minerals and rare earth elements play an important role in our climate change initiatives, particularly in applications related with energy storage. Here, we use discrete optimization approaches to design a process for the recovery of Lithium and Cobalt from battery recycling, through membrane separation. Our contribution involves proposing a Generalized Disjunctive Programming (GDP) model for the optimal design of a multistage diafiltration cascade for Li-Co separation. By solving the resulting nonconvex mixed-integer nonlinear program model to global optimality, we investigated scalability and solution quality variations with changes in the number of stages and elements per stage. Results demonstrate the computational tractability of the nonlinear GDP formulation for design of membrane separation processes while opening the door for decomposition strategies for multicomponent separation cascades. Future work aims to extend the GDP formulation to account for stage installatio... [more]

Recent advances in process design for solvent-based, post-combustion capture (PCC) processes, such as the Piperazine/Advanced Flash Stripper (PZ/AFS) process, have led to a reduction in the energy required for capture. Even though PCC processes are progressively improving in Technology Readiness Levels (TRL), with a few commercial installations, incorporating carbon capture adds cost to any operation. Hence, cost reduction will be instrumental for proliferation. The aim of this work is to improve process economics through optimization and to identify the parameters in our economic model that have the greatest impact on total cost to build and operate these systems. To that end, we investigated changes to the optimal solution and the corresponding cost of capture considering changes in the price of utilities and solvent. We found that changes in solvent price had the most effect on the cost of capture. However, re-optimizing the designs in the event of price changes did not lead to sign... [more]

The synthesis of energy systems necessitates simultaneous optimization of both design and operation across all components within the energy system. In real-world applications, this synthesis poses a mixed-integer nonlinear programming (MINLP) problem, considering nonlinear behaviours such as investment cost curves and part-load performance. The complexity increases further when seasonal energy storage is involved, as it requires temporal coupling of the full time series. Although numerous solution approaches exist to solve the synthesis problems simplified by linearization, methods for solving a full-scale problem are currently missing. In this work, we introduce a rigorous method, RiNSES4, to manage the nonlinear aspects of energy system synthesis, particularly focusing on long-term time-coupling constraints. RiNSES4 calculates the upper and lower bounds of the initial synthesis problem in two separate branches. The proposed method yields feasible solutions through upper bounds, while... [more]

Thermal separation processes, such as distillation, play a pivotal role in the chemical and petrochemical sectors, constituting a substantial portion of the industrial energy consumption. Consequently, owing to their huge application scales, these processes contribute significantly to greenhouse gas (GHG) emissions. Decarbonizing distillation units could mitigate carbon emissions substantially. Heat Pumps (HP), that recycle lower quality heat from the condenser to the reboiler by electric work present a unique opportunity to electrify distillation systems. In this research we try to answer the following question in the context of multi-component distillation – Do HPs actually reduce the effective fuel consumption or just merely shift the fuel demand from chemical industry to the power plant? If they do, what strategies consume minimum energy? To address these inquiries, we construct various simplified surrogate and shortcut models designed to effectively encapsulate the fundamental phy... [more]

In this paper, a hybrid heuristic rule-based and deterministic optimization-driven process decision framework is presented for the analysis and optimization of process flowsheets for end-to-end optimal (E2E0) pharmaceutical manufacturing. The framework accommodates various operating modes, such as batch, semi-batch and continuous, for the different unit operations that implement each manufacturing step. To address the challenges associated with solving process synthesis problems using a simulation-optimization approach, heuristic-based process synthesis rules are employed to facilitate the reduction of the superstructure into smaller sub-structures that can be more readily optimized. The practical application of the framework is demonstrated through a case study involving the end-to-end continuous manufacturing of an anti-cancer drug, lomustine. Alternative flowsheet structures are evaluated in terms of the sustainability metric, E-factor while ensuring compliance with the required pro... [more]