Actual Production Data Research Articles

Building resilient renewable power generation portfolios: The impact of diversification on investors’ risk and return

Over the coming years, variable renewables such as wind and photovoltaics will become increasingly exposed to market risks due to the gradual reduction in policy support. Building diversified portfolios of variable renewables and complementary technologies such as storage (i.e. technological diversification), and in different regions (i.e. geographical diversification) seem to be promising options to mitigate these risks. The extant literature, however, does not provide a comprehensive comparison of these two diversification strategies. Using actual production data from eight wind and photovoltaics plants across Germany from 2015 until 2017 and a storage unit for arbitrage operations, we build a quantitative model to evaluate the impact of these strategies on investors’ risk and return. In our analysis, we compare the results of two scenarios: the first with actual prices and the second which assumes prices reflecting higher shares of variable renewables in the power system. In doing so, our study provides the following important insights for investors: (1) technological diversification largely yields lower risk levels than geographical diversification, (2) maximizing the capacity factor of wind and photovoltaics is an effective way to mitigate risk, and (3) while technological diversification with another variable renewable technology is more effective under current conditions, storage gains importance for mitigating risk in times of high shares of variable renewables.

Study and Application of Diagnosis Curves of Water Channeling Patterns for Horizontal Well in Bottom-water Heavy Oil Reservoir

Researches on the water breakthrough patterns and laws of horizontal well are weak. Based on this, a typical model was established by using the geology and reservoir parameters of the heavy oil reservoir with large bottom water in the Bohai Sea. Generally, there are 4 water-out patterns for horizontal well in bottom-water heavy oil reservoir, including punctiform breakthrough waterflooding in a local horizontal section, punctiform breakthrough waterflooding in several local horizontal sections, linear breakthrough waterflooding in the whole horizontal section, and water channeling along high permeability zone. Then, the typical diagnosis curves of these water breakthrough patterns were drawn by studying WOR (water-oil ratio) and its first-order time derivative. However, there are usually value errors in the actual production data, so the interference of noise signals in production data would reduce the accuracy and reliability of water output diagnosis. Using wavelet transform method to denoise dynamic data can simultaneously guarantee both the smoothness and approximation of derivative curves. Finally, the effective measures of different water flooding modes were put forward by the case study of 22 horizontal wells in LD bottom-water reservoir. It has reference significance for efficient development of bottom-water reservoir with horizontal wells in high water cut stage.

Optimization models for operation of a steam power system in integrated iron and steel works

ABSTRACT The steam power system (SPS) is one of the most significant contributors to the energy consumption and pollutant emissions in integrated iron and steel works. In this study, a mixed-integer nonlinear programming (MINLP) model is developed to solve the problem of surplus byproduct gas utilization and reduce the economic operation cost (EOC) of the system. Fluctuations of surplus byproduct gas, dynamic demand of steam and electricity, time-of-use power price, and production safety constraints are also considered. The models have been verified by actual production data of the iron and steel works. Based on the MINLP, the results show that the reasonable use of heat value constraint will improve the utilization rate of the BFG and reduce the EOC, and the effect of equipment maintenance is discussed. The reasonable use of the heat value constraint is also discussed in the MINLP, which is helpful to improve the utilization rate of BFG and reduce the cost of economic operation.

System development for establishing shipyard mid-term production plans using backward process-centric simulation

In this paper, we propose a simulation method based on backward simulation and process-oriented simulation to take into account the characteristics of shipbuilding production, which is an order-based industry with a job shop production environment. The shipyard production planning process was investigated to analyze the detailed process, variables and constraints of mid-term production planning. Backward and process-centric simulation methods were applied to the mid-term production planning process and an improved planning process, which considers the shipbuilding characteristics, was proposed. Based on the problem defined by applying backward process-centric simulation, a system which can conduct Discrete Event Simulation (DES) was developed. The developed mid-term planning system can be linked with the existing shipyard Advanced Planning System (APS). Verification of the system was performed with the actual shipyard mid-term production data for the four ships corresponding to a one-year period.

Toward more realistic models of reservoir by cutting-edge characterization of permeability with MPS methods and deep-learning-based selection

Different sources of data are used to construct a reliable model of reservoir for oil/gas production. This model ought to be matched with the production history of reservoir and also show reliable predictions for future performance. To this end, permeability modeling (characterization of heterogeneity) is crucially important which is proved to be done by Multiple Point Statistics (MPS) recently. Furthermore, deep learning methods are massively used as a promising tool for regression applications. In this study, one MPS method is employed for generating the reservoir realizations. Realizations, alongside their simulation outputs, are utilized for training a convolutional deep network. In this manner, MPS is joined with deep learning to find the most appropriate realization(s) of the reservoir based on the fluid flow simulation. Moreover, unseen MPS realizations as well as another MPS realizations are used to verify the selection ability of trained network. The detailed architecture of convolutional network is illustrated in this study.The purpose of training this network and combination with MPS is to generate the matched realization(s) in history period that also show acceptable reservoir behavior in the future times of reservoir simulation. After training, the actual production data of selected realizations are obtained by simulation the reservoir for history and also future times. The results show that selected realizations efficiently capture the trend of reference behavior. Although these realizations lack identical permeability values, they have same texture of permeability (permeability heterogeneity). Meanwhile, they show acceptable match in reservoir simulation outputs. By proposed workflow, the uncertainty of permeability modeling is considered more exhaustively. It is done by selecting the realizations from enormous possible realizations dataset and providing a deep learning tool which is capable for screening quite large number of realizations. Interesting finding is satisfactory behavior of realization(s) in both history and future periods of reservoir performance.

Prediction Model of Coal Reservoir Pressure and its Implication for the Law of Coal Reservoir Depressurization

The main methods of coalbed methane (CBM) development are drainage and depressurization, and a precise prediction of coal reservoir pressure is thus crucial for the evaluation of reservoir potentials and the formulation of reasonable development plans. This work established a new reservoir pressure prediction model based on the material balance equation (MBE) of coal reservoir, which considers the self‐regulating effects of coal reservoirs and the dynamic change of equivalent drainage area (EDA). According to the proposed model, the reservoir pressure can be predicted based on reservoir condition data and the actual production data of a single well. Compared with traditional reservoir pressure prediction models which regard EDA as a fixed value, the proposed model can better predict the average pressure of reservoirs. Moreover, orthogonal experiments were designed to evaluate the sensitivity of reservoir parameters on the reservoir pressure prediction results of this proposed model. The results show that the saturation of irreducible water is the most sensitive parameter, followed by Langmuir volume and reservoir porosity, and Langmuir pressure is the least sensitive parameter. In addition, the pressure drop of reservoirs is negatively correlated with the saturation of irreducible water and the Langmuir volume, while it is positively correlated with porosity. This work analyzed the reservoir pressure drop characteristics of the CBM wells in the Shizhuangnan Block of the Qinshui Basin, and the results show that the CBM reservoir depressurization can be divided into three types, i.e., rapidly drop type, medium‐term stability type, and slowly drop type. The drainage features of wells were reasonably interpreted based on the comprehensive analysis of the reservoir depressurization type; the latter was coupled to the corresponding permeability dynamic change characteristics, eventually proving the applicability of the proposed model.

Electrode regulating system modeling in electrical smelting furnace using recurrent neural network with attention mechanism

Electrical smelting furnace (ESF) is the primary equipment to produce steer, nonferrous metals and other materials. In smelting process, it is beneficial to keep the smelting current stable within a reasonable range to improve product quality and reduce energy consumption. ESF is characterized by nonlinearity, strong coupling and time variation, which makes it difficult to establish accurate mathematical models. Therefore, we propose a data-driven recurrent neural network (RNN) using gated recurrent unit (GRU) with attention mechanism not only to establish the relationship between the electrode position and the smelting current but also to reveal the internal dynamic variation between three-phase currents for the electrode regulating system. Our proposed model is tested on the actual production data collected from a fused magnesium furnace (one kind of ESF) in Liaoning Province of China. Numerical results show RNN excels at processing sequential data and describing inner changes of the dynamic system; GRU alleviates long-term dependency problems in industrial big data; attention mechanism can identify and put emphasis on the crucial points containing key information in long sequential data. The results indicate that the proposed model is effective and feasible for the modeling of electrode regulating system.

Distributed wind resource assessment: Comparing measured annual energy production with predictions from computational fluid dynamics

The US Department of Energy’s Distributed Wind Resource Assessment Workshop identified predicting the annual energy production of a kilowatt-sized wind turbine as a key challenge. This article presents the methods and results for predicting the annual energy production of two 2.1 kW Skystream 3.7 wind turbines using computational fluid dynamics, in this case Meteodyn WT. When compared with actual production data, annual energy production values were uniformly underpredicted, with errors ranging from 1% to in excess of 30%, depending on the solver settings and boundary conditions. The most accurate of the simulations with errors consistently less than 10% were achieved when using recommended solver settings of neutral atmospheric stability, and roughness values derived from the US National Land Cover Database. The software was used to create an annual energy production map for the modeling domain, which could be a valuable tool in estimating the energy output and economic value of a proposed wind turbine.

Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints

A data-driven modeling method with feature selection capability is proposed for the combustion process of a station boiler under multi-working conditions to derive a nonlinear optimization model for the boiler combustion efficiency under various working conditions. In this approach, the principal component analysis method is employed to reconstruct new variables as the input of the predictive model, reduce the over-fitting of data and improve modeling accuracy. Then, a k-nearest neighbors algorithm is used to classify the samples to distinguish the data by the different operating conditions. Based on the classified data, a least square support vector machine optimized by the differential evolution algorithm is established. Based on the boiler key parameter model, the proposed model attempts to maximize the combustion efficiency under the boiler load constraints, the nitrogen oxide (NOx) emissions constraints and the boundary constraints. The experimental results based on the actual production data, as well as the comparative analysis demonstrate: (1) The predictive model can accurately predict the boiler key parameters and meet the demands of boiler combustion process control and optimization; (2) The model predictive control algorithm can effectively control the boiler combustion efficiency, the average errors of simulation are less than 5%. The proposed model predictive control method can improve the quality of production, reduce energy consumption, and lay the foundation for enterprises to achieve high efficiency and low emission.

Production forecast of a multistage fractured horizontal well by an analytical method in shale gas reservoir

Industry benefits cannot be obtained from shale gas reservoir without stimulations, due to the ultra-low porosity and permeability of shale. A series of integrated technical measures have been developed for developing shale gas economically, such as horizontal wells and multistage hydraulic fracturing. Combining the above works, we can achieve higher productivity by enlarging stimulated reservoir volume (SRV) and linking fracture network in shale gas reservoirs. In this paper, a novel analytical mathematical model for production forecast of multistage horizontal well was developed based on the seepage theory of fractured well in the dual-medium gas reservoirs. In this model, multi-scale migration mechanism and the complicated morphology of hydraulic fractures in fractured shale gas reservoir were considered. It has been closely solved by the method of well test analysis and mathematical physics. To validate the accuracy of the model in this paper, a well from Changning–Weiyuan shale gas reservoir in China is taken as a real-case application. The calculation results of the model and the actual production data of the well are in good accordance. Meanwhile, the impacts of sensitive factors including desorption, Knudsen diffusion, slip flow, stress sensitivity of micro-fractures and high-velocity non-Darcy flow within hydraulic fractures on cumulative production were analyzed. At last, fracture morphology has been optimized through the model.

Prediction and Analysis of Traction Battery Decommissioning Amount of NEVs in China

With lengthening of new energy vehicles’ service life, the decommissioning amount of traction batteries is on an annual increase. Recycling of waste and old traction batteries has gained growing attention from the national government and the industry. Rational and accurate prediction of traction battery decommissioning amount is of vital importance to recycling of traction batteries. Based on the actual production data and market survey results of new energy vehicles and traction batteries, a traction battery decommissioning amount prediction model is built according to different vehicles models. The model can be used to analyze and predict the decommissioning amount of traction batteries in China and provide references for recycling management and industrial layout of traction batteries.

Symmetry variable taper work roll technology for silicon steel profile control in hot strip mills

ABSTRACT Strip profile control in the hot rolling process plays an important role in guaranteeing high-precision profiles for silicon steel production in cold strip mills. A symmetry variable taper (SVT) work roll for the downstream finishing stands of hot strip mills is designed, which can be used for long stroke reciprocating shifting of work rolls, resulting in decreased edge drop and more uniform work roll wear contour. A finite element simulation model of 4-high mill based on ABAQUS was established, which improved the control characteristics of each process parameter for the strip profile when using the SVT work roll. The actual production data of the 1580 hot strip rolling mill equipped with SVT work rolls showed that the crown C25 of non-oriented silicon steel was reduced by about 5%, the edge drop was reduced by more than 15%, and the crown qualified rate was increased by 10%.

Analysis Method for Tracer Production Curve in Fractured-vuggy Reservoir

The fracture-vuggy carbonate reservoir in Tahe oilfield is unique and difficult to be analyzed by conventional tracer analysis technology. Based on the geological characteristics of fracture and vug in reservoirs, a theoretical model is established by applying fluent to simulate the flow characteristics of fluids in fracture-vug, so as to analyze the characteristics of tracers under different conditions: The output of tracer under the condition of single fracture shows kurtosis, while under the condition of single vug without kurtosis; The combination of many fractures can either enhance the peak height or show multiple peaks, while the combination of multiple vugs still shows no kurtosis but only extends the range; The output characteristics of series combination of fracture/vug were more influenced by themselves. The smaller the volume of vug is, the less the fractures were influenced, and thus the production becomes more and more similar to fractures; while the rest of them show vug characteristics. Through applying the tracer production curve analysis theory to the actual production data of the field tracer test, and establishing the fracture tunnel ratio and the dominant channel direction judgement method to characterize the reservoir flow path, we found that the ratio of the fracture and vug in the target area is 1:9; the direction of the dominant channel is determined by the size of the Interdistance intercepting area of the injection production well - the larger the section area is, the better the channel is. It indicates that the analytical method of tracer production curve in fracture-vug reservoir is applicable, and it can lay a foundation for improving the theory of tracer production characteristics analysis in fracture-vug reservoirs.

Engineering scale design of gas-solid heat transfer in vertical packed bed

An engineering heat transfer model was applied to calculate heat transfer in vertical sinter packed bed associated with sinter cooling process for ironmaking requirement. Ambient temperature air and hot sinter particles direct contact counter-current with recovery heat from hot sinter moving particles in vertical packed bed. This research was used to measure the gas-solid heat transfer characteristics, temperature field distribution, operation and mechanism design parameters in the vertical sinter packed bed. The actual production data was used to calibrate the heat transfer model in this research.

Rolling Force Prediction of Hot Rolling Based on GA‐MELM

In the hot continuous rolling process, the main factor affecting the actual thickness of strip is the rolling force. The precision of rolling force calculation is the key to realize accurate on‐line control. However, because of the complexity and nonlinearity of the rolling process, as well as many influencing factors, the theoretical analysis of the traditional rolling force prediction model often needs to be simplified and hypothesized. This leads to the incompleteness of the mathematical model and the deviation between the calculated results and the actual working conditions. In this paper, a rolling force prediction method based on genetic algorithm (GA), particle swarm optimization algorithm (PSO), and multiple hidden layer extreme learning machine (MELM) is proposed, namely, PSO‐GA‐MELM algorithm, which takes MELM as the basic model for rolling force prediction. In the modeling process, GA is used to determine the optimal number of hidden layers and the optimal number of hidden nodes, and PSO is used to search for the optimal input weights and biases. This method avoids the influence of human intervention on the model and saves the modeling time. This paper takes the actual production data of BaoSteel 2050 production line as experimental data, and the experimental results indicate that the algorithm can be effectively used to determine the optimal network structure of MELM. The rolling force prediction model trained by the algorithm has excellent performance in prediction accuracy, computational stability, and the number of hidden nodes and is applicable to the prediction of rolling force in hot continuous rolling process.

Optimal Control Strategy of Multi-Stage Pharmaceutical

Abstract The fermentation production process is the basis for ensuring product quality in pharmaceutical production. There are many variable parameters such as temperature, number of revolutions and PH value in the fermentation production process, therefore, it is difficult to describe the complex problems through precise mathematical models. Since the process has to go through four different steps, each of which is a nonlinear time-varying dynamic system, the four steps constitute a large dynamic system, and the global modeling accuracy cannot be guaranteed; and the optimization problem of the nonlinear time-varying dynamic system is also a problem. In order to solve the above problems, the paper proposes to use the BP (Back Propagation）neural network modeling method to screen the input variables of the system with large effect on the basis of the actual production data of a pharmaceutical factory. Then, based on the process parameters of the fermentation and the output of the product quality index, a multi-stage mapping model based on LSSVM (Least Squares Support Vector Machine) was established. Finally, the PSO (Particle Swarm Optimization) algorithm is used to optimize the parameters of the model.The simulation results show that the model obtained by parameter optimization can achieve better prediction results.

Fully coupled fluid-solid numerical simulation of stimulated reservoir volume (SRV)-fractured horizontal well with multi-porosity media in tight oil reservoirs

A mutual coupling effect exists between the flow and stress fields in tight oil reservoir development. In terms of the characteristics of multi-porosity media (the matrix, natural fractures and network fractures) for stimulated reservoir volume (SRV)-fractured horizontal wells in tight oil reservoirs, considering the dual porosities of matrix and natural fractures, and based on the flowing feature of a discrete-fracture mode (DFM) fracture network stimulated system, a numerical simulation method based on fully coupled fluid-solid is introduced. According to the effective stress principle and regional flow characteristics, the fully coupled fluid-solid mathematical model considering the multi-porosity media characteristics of complex fracture networks is presented. The fully coupling numerical solution for the stress and flow fields is solved by the finite element method. The accuracy of this model is verified by comparing the finite differential model, non-coupled model and actual production data, respectively. And analyzing the computational time needed to solve the model under different solution methods and complexity levels. The variation of stress-strain and reservoir physical property parameters is revealed. For the non-coupled and fully coupled model, the productivity and formation pressure differences are compared and analysed. The results show that when considering the stress field, the initial production rate of an SRV-fractured horizontal well is large but declines quickly, and it has fundamentally different characteristics from the traditional flow model. Our research provides theoretical and technical guidance for the efficient development of unconventional resources.

Multi-Objective Optimization of Cost Saving and Emission Reduction in Blast Furnace Ironmaking Process

Due to the increasing environmental pressures, one of the most direct and effective way to achieve emission reduction is to reduce the CO2 emissions of the blast furnace process in the iron and steel industry. Based on the substance conservation and energy conservation of ironmaking process and the engineering method, the carbon loss model was firstly established to calculate the amount of solution loss. Based on this model, the blast furnace emission reduction optimization mathematical model with the cost and CO2 emissions as objective functions was then established using the multiple-objective optimization method. The optimized results were obtained by using the GRG (Generalized Reduced Gradient) nonlinear solving method. The optimization model was applied to the B# blast furnace of BayiSteel in China. The optimization model was verified by comparing the optimized results with the actual production data. The optimization model was then applied to analyze the effects of coke ratio, coal rate, blast temperature and other factors on the cost, CO2 emission and solution loss, and some measures to save cost, reduce emissions and reduce solution loss have been proposed.

High precision shape model and presetting strategy for strip hot rolling

The effect of stress release on the shape and deformation of strip between stands are rarely considered or quantified in the traditional strip shape model. In order to make up this deficiency, a high precision shape model is established by the mechanism of the strip shape forming and the deformation of roll system in this paper. Special concentration is placed on the effect of roll wear, roll thermal expansion, metal transverse flow and stress release on the shape. In addition, a tolerance factor which is used to describe the attenuation and tolerance abilities of each stand for longitudinal strain difference is proposed as a foundation for a presetting strategy. Compared to the traditional presetting strategies using the empirical coefficient or Shohet theory, the presetting strategy on the basis of the tolerance factor can better play the control ability of each stand. A systematical description of the presetting strategy and the modeling process of the high precision shape model is given in the present study. The calculated results agree well with experiments, and the actual production data show that the shape model has satisfactory accuracy.

A RQPSO Algorithm for Multiphase Equilibrium Calculation in the KIVCET Process

The prediction of phase configurations and the optimization of operating parameters in the metallurgical process are normally achieved by the multiphase equilibrium calculation (MEC), which is formulated as a constrained optimization problem based on the principle of Gibbs free energy minimization. A revised quantum-behaved particle swarm optimization (RQPSO) algorithm has been proposed to solve the optimization problem using three-part improved strategies. Based on the KIVCET smelting characteristics, a MEC model for the KIVCET process is established and solved using the RQPSO algorithm. The calculated and industrial data of the lead grade are 96.20% and 96.33%, respectively, those of the matte grade are 19.39% and 19.68%, and the mass fractions of Pb in the predicted and industrial matte are 3.96% and 3.34%, respectively. The calculated results of the phase configuration are consistent with the actual production data, which indicates that the MEC model and RQPSO algorithm are accurate and reliable.