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Keywords = carbon price sequence

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17 pages, 1706 KiB  
Article
Point and Interval Forecasting of Coal Price Adopting a Novel Decomposition Integration Model
by Junjie Liu and Lang Liu
Energies 2024, 17(16), 4166; https://doi.org/10.3390/en17164166 - 21 Aug 2024
Cited by 1 | Viewed by 1298
Abstract
Accurate and trustworthy forecasting of coal prices can offer theoretical support for the rational planning of coal industry output, which is of great importance in ensuring a stable and sustainable energy supply and in achieving carbon neutrality targets. This paper proposes a novel [...] Read more.
Accurate and trustworthy forecasting of coal prices can offer theoretical support for the rational planning of coal industry output, which is of great importance in ensuring a stable and sustainable energy supply and in achieving carbon neutrality targets. This paper proposes a novel decomposition integration model, called VCNQM, to perform point and interval forecasting of coal price by a combination of variational modal decomposition (VMD), chameleon swarm algorithm (CSA), N-BEATS, and quantile regression. Initially, the variational modal decomposition is enhanced by the chameleon swarm algorithm for decomposing the coal price sequence. Then, N-BEATS is used to forecast each subsequence of coal prices, integrating all results to obtain a point forecast of coal prices. Next, interval forecasting of coal prices is achieved through quantile regression. Finally, to demonstrate the superiority of the VCNQM model’s prediction, we make a cross-comparison about predictive performance between the VCNQM model and other benchmark models. According to the experimental findings, we demonstrate the following: after the decomposition by CSA-VMD, the coal price subseries’ fluctuation is significantly weakened; using quantile regression provides a reliable interval prediction, which is superior to point prediction; the predicted interval coverage probability (PICP) is higher than the confidence level of 90%; the share power industry index and coal industry index have the greatest impact on coal prices in China; compared to these benchmark models, the VCNQM model’s prediction errors are all reduced. Therefore, we conclude that when forecasting coal prices, the VCNQM model has an accurate and reliable prediction. Full article
(This article belongs to the Section C: Energy Economics and Policy)
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26 pages, 7408 KiB  
Article
A Hybrid Model for Carbon Price Forecasting Based on Improved Feature Extraction and Non-Linear Integration
by Yingjie Zhu, Yongfa Chen, Qiuling Hua, Jie Wang, Yinghui Guo, Zhijuan Li, Jiageng Ma and Qi Wei
Mathematics 2024, 12(10), 1428; https://doi.org/10.3390/math12101428 - 7 May 2024
Cited by 4 | Viewed by 1997
Abstract
Accurately predicting the price of carbon is an effective way of ensuring the stability of the carbon trading market and reducing carbon emissions. Aiming at the non-smooth and non-linear characteristics of carbon price, this paper proposes a novel hybrid prediction model based on [...] Read more.
Accurately predicting the price of carbon is an effective way of ensuring the stability of the carbon trading market and reducing carbon emissions. Aiming at the non-smooth and non-linear characteristics of carbon price, this paper proposes a novel hybrid prediction model based on improved feature extraction and non-linear integration, which is built on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fuzzy entropy (FuzzyEn), improved random forest using particle swarm optimisation (PSORF), extreme learning machine (ELM), long short-term memory (LSTM), non-linear integration based on multiple linear regression (MLR) and random forest (MLRRF), and error correction with the autoregressive integrated moving average model (ARIMA), named CEEMDAN-FuzzyEn-PSORF-ELM-LSTM-MLRRF-ARIMA. Firstly, CEEMDAN is combined with FuzzyEn in the feature selection process to improve extraction efficiency and reliability. Secondly, at the critical prediction stage, PSORF, ELM, and LSTM are selected to predict high, medium, and low complexity sequences, respectively. Thirdly, the reconstructed sequences are assembled by applying MLRRF, which can effectively improve the prediction accuracy and generalisation ability. Finally, error correction is conducted using ARIMA to obtain the final forecasting results, and the Diebold–Mariano test (DM test) is introduced for a comprehensive evaluation of the models. With respect to carbon prices in the pilot regions of Shenzhen and Hubei, the results indicate that the proposed model has higher prediction accuracy and robustness. The main contributions of this paper are the improved feature extraction and the innovative combination of multiple linear regression and random forests into a non-linear integrated framework for carbon price forecasting. However, further optimisation is still a work in progress. Full article
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27 pages, 2400 KiB  
Article
A Supply Chain Coordination Optimization Model with Revenue Sharing and Carbon Awareness
by Nistha Kumari, Yogendra Kumar Rajoria, Anand Chauhan, Satya Jeet Singh, Anubhav Pratap Singh and Vineet Kumar Sharma
Sustainability 2024, 16(9), 3697; https://doi.org/10.3390/su16093697 - 28 Apr 2024
Cited by 6 | Viewed by 1949
Abstract
The present study explores the impact of carbon emissions on supply chain coordination, where the supply chain entities are a retailer and a distributor. The study also involves two types of systems, namely centralized and decentralized. A centralized system computes the profit of [...] Read more.
The present study explores the impact of carbon emissions on supply chain coordination, where the supply chain entities are a retailer and a distributor. The study also involves two types of systems, namely centralized and decentralized. A centralized system computes the profit of the entire supply chain, including the profit of a retailer and a distributor, using the traditional optimization technique. In contrast, a decentralized system computes the profit of both a retailer and a distributor independently and uses the Stackelberg sequence for profit optimization. According to the Stackelberg sequence, one entity is considered a leader and the other a follower. When the profit in both systems is compared, it is found to be higher in the centralized system. So, to coordinate the system, a revenue-sharing contract is applied to coordinate the supply chain under a stock–time–price-sensitive demand rate. Finally, a carbon emission cost is implemented to the profits of both systems to make the model more sustainable. The main objective of the research is to optimize the profit of the supply chain by considering the concept of revenue-sharing contracts and making the system more sustainable through the implementation of carbon emission cost. The overall study concludes that the revenue-sharing fraction δ helps in coordinating the system and 0.4 is the value of the revenue-sharing fraction δ that perfectly coordinates the system. Due to this coordination, both the parties will gain profit, i.e., retailer and distributor, and this whole phenomenon increases the profit of the supply chain. A sensitivity analysis is also performed to check the stability of the model, and the model is found to be quite stable. A numerical example is illustrated, providing the result of the model. Full article
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20 pages, 2769 KiB  
Article
Low-Carbon Optimal Scheduling of Integrated Energy System Considering Multiple Uncertainties and Electricity–Heat Integrated Demand Response
by Hongwei Li, Xingmin Li, Siyu Chen, Shuaibing Li, Yongqiang Kang and Xiping Ma
Energies 2024, 17(1), 245; https://doi.org/10.3390/en17010245 - 3 Jan 2024
Cited by 4 | Viewed by 1805
Abstract
To realize the low-carbon operation of integrated energy systems (IESs), this paper proposes a low-carbon optimal scheduling method. First of all, considering the integrated demand response of price-based electricity and heating, an economic scheduling model of the IES integrated demand response based on [...] Read more.
To realize the low-carbon operation of integrated energy systems (IESs), this paper proposes a low-carbon optimal scheduling method. First of all, considering the integrated demand response of price-based electricity and heating, an economic scheduling model of the IES integrated demand response based on chance-constrained programming is proposed to minimize the integrated operating cost in an uncertain environment. Through the comprehensive demand response model, the impact of the demand response ratio on the operating economy of the IES is explored. Afterward, the carbon emission index is introduced, and gas turbines and energy storage devices are used as the actuators of multi-energy coupling to further explore the potential interactions between the coupling capacities of various heterogeneous energy sources and carbon emissions. Finally, the original uncertainty model is transformed into a mixed-integer linear-programming model and solved using sequence operation theory and the linearization method. The results show that the operating economy of the IES is improved by coordinating the uncertainty of the integrated demand response and renewable energy. In addition, the tradeoff between the working economy and reliability of the EIS can be balanced via the setting of an appropriate confidence level for the opportunity constraints. Full article
(This article belongs to the Section F: Electrical Engineering)
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19 pages, 5408 KiB  
Article
A Multi-Strategy Integration Prediction Model for Carbon Price
by Hongwei Dong, Yue Hu, Yihe Yang and Wenjing Jiang
Energies 2023, 16(12), 4613; https://doi.org/10.3390/en16124613 - 9 Jun 2023
Cited by 8 | Viewed by 2284
Abstract
Carbon price fluctuations significantly impact the development of industries, energy, agriculture, and stock investments. The carbon price possesses the features of nonlinearity, non-stationarity, and high complexity as a time series. To overcome the negative impact of these characteristics on prediction and to improve [...] Read more.
Carbon price fluctuations significantly impact the development of industries, energy, agriculture, and stock investments. The carbon price possesses the features of nonlinearity, non-stationarity, and high complexity as a time series. To overcome the negative impact of these characteristics on prediction and to improve the prediction accuracy of carbon price series, a combination prediction model named Lp-CNN-LSTM, which utilizes both convolutional neural networks and long short-term memory networks, has been proposed. Strategy one involved establishing distinct models of CNN-LSTM and LSTM to analyze high-frequency and low-frequency carbon price sequences; the combination of output was integrated to predict carbon prices more precisely. Strategy two comprehensively considered the economic and technical indicators of carbon price sequences based on the Pearson correlation coefficient, while the Multi-CNN-LSTM model selected explanatory variables that strongly correlated with carbon prices. Finally, a predictive model for a combination of carbon prices was developed using Lp-norm. The empirical study focused on China’s major carbon markets, including Hubei, Guangdong, and Shanghai. According to the error indicators, the performance of the Lp-CNN-LSTM model was superior to individual strategy prediction models. The Lp-CNN-LSTM model has excellent accuracy, superiority, and robustness in predicting carbon prices, which can provide a necessary basis for revising carbon pricing strategies, regulating carbon trading markets, and making investment decisions. Full article
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18 pages, 8592 KiB  
Review
Research Progress on New Types of H2TiO3 Lithium-Ion Sieves: A Review
by Ying Li, Zhen Yang and Peihua Ma
Metals 2023, 13(5), 977; https://doi.org/10.3390/met13050977 - 18 May 2023
Cited by 13 | Viewed by 4439
Abstract
The advantages of new types of H2TiO3 lithium-ion sieves, including excellent adsorption performance, high-efficiency Li+-ion selectivity, reliable regeneration, environmental friendliness, and easy preparation, have attracted considerable attention. Currently, the prices of lithium carbonate and other related products are [...] Read more.
The advantages of new types of H2TiO3 lithium-ion sieves, including excellent adsorption performance, high-efficiency Li+-ion selectivity, reliable regeneration, environmental friendliness, and easy preparation, have attracted considerable attention. Currently, the prices of lithium carbonate and other related products are rapidly increasing, so the use of H2TiO3 lithium-ion sieves to extract lithium resources in salt lake brine has become a crucial strategy. H2TiO3 lithium-ion sieve is a layered double hydroxide with a 3R1 sequence to arrange oxygen layers. Its adsorption mechanism involves the breaking of surface O-H bonds and the formation of O-Li bonds. This study provides a theoretical basis for developing high-efficiency lithium-ion sieves. This article also summarizes the influencing factors for the synthesis process of H2TiO3, which can seriously influence the adsorption performance, and offers experimental verification for the preparation of H2TiO3 lithium-ion sieves. H2TiO3 lithium-ion sieves prepared from anatase using a reasonable method show the largest adsorption capacity. In addition, effective ways to recycle H2TiO3 are outlined, which provide a guarantee for its industrial application. Finally, this paper summarizes the full text and points out future research directions for H2TiO3 lithium-ion sieves. Full article
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21 pages, 3451 KiB  
Article
Carbon Trading Price Prediction of Three Carbon Trading Markets in China Based on a Hybrid Model Combining CEEMDAN, SE, ISSA, and MKELM
by Haoran Zhao and Sen Guo
Mathematics 2023, 11(10), 2319; https://doi.org/10.3390/math11102319 - 16 May 2023
Cited by 1 | Viewed by 1815
Abstract
Carbon trading has been deemed as the most effective mechanism to mitigate carbon emissions. However, during carbon trading market operation, competition among market participants will inevitably occur; hence, the precise forecasting of the carbon trading price (CTP) has become a significant element in [...] Read more.
Carbon trading has been deemed as the most effective mechanism to mitigate carbon emissions. However, during carbon trading market operation, competition among market participants will inevitably occur; hence, the precise forecasting of the carbon trading price (CTP) has become a significant element in the formulation of competition strategies. This investigation has established a hybrid CTP forecasting framework combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE) method, improved salp swarm algorithm (ISSA), and multi-kernel extreme learning machine (MKELM) methods to improve forecasting accuracy. Firstly, the initial CTP data sequence is disintegrated into several intrinsic mode functions (IMFs) and a residual sequence by a CEEMDAN method. Secondly, to save calculation time, SE method has been utilized to reconstruct the IMFs and the residual sequence into new IMFs. Thirdly, the new IMFs are fed into the MKELM model, combing RBF and the poly kernel functions to utilize their superior learning and generalization abilities. The parameters of the MKELM model are optimized by ISSA, combining dynamic inertia weight and chaotic local searching method into the SSA to enhance the searching speed, convergence precision, as well as the global searching ability. CTP data in Guangdong, Shanghai, and Hubei are selected to prove the validity of the established CEEMDAN-SE-ISSA-MKELM model. Through a comparison analysis, the established CEEMDAN-SE-ISSA-MKELM model performs the best with the smallest MAPE and RMSE values and the highest R2 value, which are 0.76%, 0.53, and 0.99, respectively, for Guangdong,. Thus, the presented model would be extensively applied in CTP forecasting in the future. Full article
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23 pages, 3062 KiB  
Article
Liner-Shipping Network Design with Emission Control Areas: A Real Case Study
by Xiangang Lan, Qin Tao and Xincheng Wu
Sustainability 2023, 15(4), 3734; https://doi.org/10.3390/su15043734 - 17 Feb 2023
Cited by 6 | Viewed by 3207
Abstract
In recent years, liner-shipping companies have faced a traditional trade-off between cost and emission (CO2 and SOX) reduction. This study considers this element to construct a liner-shipping network design model which includes a package-cargo transport plan, route allocation, and route [...] Read more.
In recent years, liner-shipping companies have faced a traditional trade-off between cost and emission (CO2 and SOX) reduction. This study considers this element to construct a liner-shipping network design model which includes a package-cargo transport plan, route allocation, and route design. The objective is to maximize profit by selecting the ports to be visited, the sequence of port visits, the cargo flows between ports, and the number/operating speeds of vessels. In addition, emission control areas (ECAs) exist in the liner network. With reference to the idea of the column generation algorithm, this study proposed a heuristic algorithm based on empirical data through a real case calculation and selected the optimal scheme, which is in-line with both economic and environmental benefits. The results show that the model and optimization method are feasible and provide an effective solution for the liner network design of shipping companies, while also considering environmental factors. In addition, the effects of the number of ECAs, inter-port origin-destination (OD) demand, freight rate, fuel price, and carbon prices on the design of transport networks are discussed to provide a reference for the operation of shipping companies and government decision-making. Full article
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20 pages, 5223 KiB  
Article
Energy Price Prediction Integrated with Singular Spectrum Analysis and Long Short-Term Memory Network against the Background of Carbon Neutrality
by Di Zhu, Yinghong Wang and Fenglin Zhang
Energies 2022, 15(21), 8128; https://doi.org/10.3390/en15218128 - 31 Oct 2022
Cited by 7 | Viewed by 2011
Abstract
In the context of international carbon neutrality, energy prices are affected by several nonlinear and nonstationary factors, making it challenging for traditional forecasting models to predict energy prices effectively. The existing literature mainly uses linear models or a combination of multiple models to [...] Read more.
In the context of international carbon neutrality, energy prices are affected by several nonlinear and nonstationary factors, making it challenging for traditional forecasting models to predict energy prices effectively. The existing literature mainly uses linear models or a combination of multiple models to forecast energy prices. For the nonlinear relationship between variables and the mining of historical data information, the prediction strategy and accuracy of the existing literature need to be improved. Thus, this paper improves the prediction accuracy of energy prices by developing a “decomposition-reconstruction-integration” thinking strategy that affords medium- and short-term energy price prediction based on carbon constraint, eigenvalue transformation and deep learning neural networks. Considering 2011–2020 as the research period, the prices for traditional energy resources and polysilicon in clean photovoltaic energy raw materials are selected as representatives. Based on energy price decomposition using the Singular Spectrum Analysis (SSA) method, and combining it with Learning Vector Quantization (LVQ) cluster technology, the decomposed quantities are aggregated into price sequences with different characteristics. Additionally, the carbon intensity is considered the leading market’s overall constraint, which is input with the processed price data into a Long Short-Term Memory network (LSTM) model for training. Thus, the SSA-LSTM combined forecasting model is developed to predict the energy price under carbon neutrality. Four indices are employed to evaluate the prediction accuracy: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and R-squared. The results highlight the following observations. (1) Using a sequence decomposition clustering strategy significantly improves the model’s prediction accuracy. This strategy enhances predicting the overall trend of the price series and the changes in different periods. For coal price, the RMSE value decreased from 0.135 to 0.098, the MAE value decreased from 0.087 to 0.054, the MAPE value decreased from 0.072 to 0.064, and the R-squared value increased from 0.643 to 0.725. Regarding the polysilicon price, the RMSE value decreased from 0.121 to 0.096, the MAE value decreased from 0.068 to 0.064, the MAPE value decreased from 0.069 to 0.048, and the R-squared value increased from 0.718 to 0.764. (2) The prediction effect is better in the case of carbon constraint. Considering “carbon emission intensity” as the overall constraint of the leading market, it can effectively explore the typical characteristics of energy price information. Four evaluation indicators show that the accuracy of the model prediction can be improved by more than 3%. (3) When the proposed SSA-LSTM model is used to predict both prices, the results show that the evaluation index of the prediction error remained at about 1%, while the model’s accuracy was high. This also proves that the proposed model can predict traditional energy prices and new energy sources such as solar energy. Full article
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18 pages, 6454 KiB  
Article
Identification of Breakpoints in Carbon Market Based on Probability Density Recurrence Network
by Mengrui Zhu, Hua Xu, Xingyu Gao, Minggang Wang, André L. M. Vilela and Lixin Tian
Energies 2022, 15(15), 5540; https://doi.org/10.3390/en15155540 - 30 Jul 2022
Cited by 1 | Viewed by 1815
Abstract
The scientific judgement of the structural abrupt transition characteristics of the carbon market price is an important means to comprehensively analyze its fluctuation law and effectively prevent carbon market risks. However, the existing methods for identifying structural changes of the carbon market based [...] Read more.
The scientific judgement of the structural abrupt transition characteristics of the carbon market price is an important means to comprehensively analyze its fluctuation law and effectively prevent carbon market risks. However, the existing methods for identifying structural changes of the carbon market based on carbon price data mostly regard the carbon price series as a deterministic time series and pay less attention to the uncertainty implied by the carbon price series. We propose a framework for identifying abrupt transitions in the carbon market from the perspective of a complex network by considering the influence of random factors on the carbon price series, expressing the carbon price series as a sequence of probability density functions, using the distribution of probability density to reveal the uncertainty information implied by carbon price series and constructing a recurrence network of carbon price probability density. Based on the community structure, the break index and statistical test method are defined. The simulation verifies the effectiveness and superiority of the method compared with traditional methods. An empirical analysis uses the carbon price data of the European Union carbon market and seven pilot carbon markets in China. The results show many abrupt transitions in the carbon price series of the two markets, whose occurrence period is closely related to major events. Full article
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24 pages, 1146 KiB  
Review
Using Real Options Thinking to Value Investment Flexibility in Carbon Capture and Utilization Projects: A Review
by Hanne Lamberts-Van Assche and Tine Compernolle
Sustainability 2022, 14(4), 2098; https://doi.org/10.3390/su14042098 - 12 Feb 2022
Cited by 14 | Viewed by 5076
Abstract
Carbon capture and utilization (CCU) is one of the key technologies that may help to reduce industrial emissions. However, the deployment of CCU is hampered by various barriers, including high levels of technical, policy and market uncertainty. The real options theory (ROT) provides [...] Read more.
Carbon capture and utilization (CCU) is one of the key technologies that may help to reduce industrial emissions. However, the deployment of CCU is hampered by various barriers, including high levels of technical, policy and market uncertainty. The real options theory (ROT) provides a method to account for these uncertainties and introduce flexibility in the investment decision by allowing decisions to be changed in response to the evolution of uncertainties. ROT is already being applied frequently in the evaluation of renewable energy or carbon capture and storage (CCS) projects, e.g., addressing the uncertainty in the price of CO2. However, ROT has only found a few applications in the CCU literature to date. Therefore, this paper investigates the specific types of uncertainty that arise with the utilization of CO2, identifies the types of real options present in CCU projects and discusses the applied valuation techniques. Research gaps are identified in the CCU literature and recommendations are made to fill these gaps. The investment decision sequence for CCU projects is shown, together with the uncertainties and flexibility options in the CCU projects. This review can support the real options-based evaluations of the investment decisions in CCU projects to allow for flexibility and uncertainty. Full article
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20 pages, 3151 KiB  
Article
Improved Multi-Scale Deep Integration Paradigm for Point and Interval Carbon Trading Price Forecasting
by Jujie Wang and Shiyao Qiu
Mathematics 2021, 9(20), 2595; https://doi.org/10.3390/math9202595 - 15 Oct 2021
Cited by 6 | Viewed by 1824
Abstract
The forecast of carbon trading price is crucial to both sellers and purchasers; multi-scale integration models have been used widely in this process. However, these multi-scale models ignore the feature reconstruction process as well as the residual part and also they often focus [...] Read more.
The forecast of carbon trading price is crucial to both sellers and purchasers; multi-scale integration models have been used widely in this process. However, these multi-scale models ignore the feature reconstruction process as well as the residual part and also they often focus on the linear integration. Meanwhile, most of the models cannot provide prediction interval which means they neglect the uncertainty. In this paper, an improved multi-scale nonlinear integration model is proposed. The original dataset is divided into some subgroups through variational mode decomposition (VMD) and all the subgroups will go through sample entropy (SE) process to reconstruct the features. Then, random forest and long-short term memory (LSTM) integration are used to model feature sub-sequences. For the residual part, LSTM residual correction strategy based on white noise test corrects residuals to obtain point prediction results. Finally, Gaussian process (GP) is applied to get the prediction interval estimate. The result shows that compared with some other methods, the proposed method can obtain satisfying accuracy which has the minimum statistical error. So, it is safe to conclude that the proposed method is able to efficiently predict the carbon price as well as to provide the prediction interval estimate. Full article
(This article belongs to the Special Issue Applied Data Analytics)
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20 pages, 1981 KiB  
Article
Petroleum Reservoir Control Optimization with the Use of the Auto-Adaptive Decision Trees
by Edyta Kuk, Jerzy Stopa, Michał Kuk, Damian Janiga and Paweł Wojnarowski
Energies 2021, 14(18), 5702; https://doi.org/10.3390/en14185702 - 10 Sep 2021
Cited by 12 | Viewed by 2761
Abstract
The global increase in energy demand and the decreasing number of newly discovered hydrocarbon reservoirs caused by the relatively low oil price means that it is crucial to exploit existing reservoirs as efficiently as possible. Optimization of the reservoir control may increase the [...] Read more.
The global increase in energy demand and the decreasing number of newly discovered hydrocarbon reservoirs caused by the relatively low oil price means that it is crucial to exploit existing reservoirs as efficiently as possible. Optimization of the reservoir control may increase the technical and economic efficiency of the production. In this paper, a novel algorithm that automatically determines the intelligent control maximizing the NPV of a given production process was developed. The idea is to build an auto-adaptive parameterized decision tree that replaces the arbitrarily selected limit values for the selected attributes of the decision tree with parameters. To select the optimal values of the decision tree parameters, an AI-based optimization tool called SMAC (Sequential Model-based Algorithm Configuration) was used. In each iteration, the generated control sequence is introduced into the reservoir simulator to compute the NVP, which is then utilized by the SMAC tool to vary the limit values to generate a better control sequence, which leads to an improved NPV. A new tool connecting the parameterized decision tree with the reservoir simulator and the optimization tool was developed. Its application on a simulation model of a real reservoir for which the CCS-EOR process was considered allowed oil production to be increased by 3.5% during the CO2-EOR phase, reducing the amount of carbon dioxide injected at that time by 16%. Hence, the created tool allowed revenue to be increased by 49%. Full article
(This article belongs to the Special Issue Management of High Water Cut and Mature Petroleum Reservoirs)
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20 pages, 5508 KiB  
Article
Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm
by Jianguo Zhou and Dongfeng Chen
Sustainability 2021, 13(9), 4896; https://doi.org/10.3390/su13094896 - 27 Apr 2021
Cited by 53 | Viewed by 4298
Abstract
Effective carbon pricing policies have become an effective tool for many countries to encourage emission reduction. An accurate carbon price prediction model is helpful for the implementation of energy conservation and emission reduction policies and the decision-making of governments and investors. However, it [...] Read more.
Effective carbon pricing policies have become an effective tool for many countries to encourage emission reduction. An accurate carbon price prediction model is helpful for the implementation of energy conservation and emission reduction policies and the decision-making of governments and investors. However, it is difficult for a single prediction model to achieve high prediction accuracy because of the high complexity of the carbon price series. Many studies have proved the nonlinear characteristics of carbon trading prices, but there are very few studies on the chaotic nature of carbon price series. As a consequence, this paper proposes an innovative hybrid model for carbon price prediction. A decomposition-reconstruction-prediction-integration scheme is designed to predict carbon prices. Firstly, several intrinsic mode functions (IMFs) and one residue were obtained from the raw data decomposed by ICEEMDAN. Next, the decomposed subsection is reconstructed into a new sequence according to the calculation results by the Lempel-Ziv complexity algorithm. Then, considering the chaotic characteristics of sequence, the input variables of the models are determined through the phase space reconstruction (PSR) algorithm combined with the partial autocorrelation function (PACF). Finally, the Sparrow search algorithm (SSA) is introduced to optimize the extreme learning machine (ELM) model, which is applied in the carbon price prediction for the purpose of verifying the validity of the proposed combination model, which is applied to the pilots of Hubei, Beijing, and Guangdong. The empirical results show that the combination model outperformed the 13 other models in predicting accuracy, speed, and stability. The decomposition-reconstruction-prediction-integration strategy is a method for predicting the carbon price efficiently. Full article
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21 pages, 1471 KiB  
Review
Conservation of Ecosystem Services in Argiudolls of Argentina
by Marcelo Germán Wilson, Alejandro Esteban Maggi, Mario Guillermo Castiglioni, Emmanuel Adrián Gabioud and María Carolina Sasal
Agriculture 2020, 10(12), 649; https://doi.org/10.3390/agriculture10120649 - 19 Dec 2020
Cited by 22 | Viewed by 4953
Abstract
Mollisols are a fundamental component of global agricultural production. In the Argentine Pampas region, 65% of the Mollisols belong to Argiudoll great group. These soils have an agricultural aptitude, with limitations given mainly by varying thickness of the top horizon A as a [...] Read more.
Mollisols are a fundamental component of global agricultural production. In the Argentine Pampas region, 65% of the Mollisols belong to Argiudoll great group. These soils have an agricultural aptitude, with limitations given mainly by varying thickness of the top horizon A as a result of the severity of water erosion depending on its site in the landscape layered on an argillic B horizon. Over the last three decades, Pampean agriculture has been widespread because of a modern technological matrix characterized by transgenic crops, and increasing use of fertilizers and pesticides. Large changes have taken place in crop sequence composition, toward the disappearance of pastures and the rapid expansion of soybean monoculture due to the upward trend of the international price of this commodity. This review contributes to an alertness regarding the significance of the soil degradation problem, in terms of decline in soil fertility and structural condition, decrease in size of soil aggregates, surface and subsurface compaction, decrease in organic carbon content, soil and water contamination, reduction of infiltration rate and structure stability, causing an increase in water losses through surface runoff and water erosion and lost ecosystem services. Additionally, a set of sustainable land management practices and legal aspects is shown. Full article
(This article belongs to the Special Issue Conservation Agriculture for Ecosystem Services)
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