Two-phase Decomposition Research Articles

Load forecasting for regional integrated energy system based on two-phase decomposition and mixture prediction model

Current load forecasting methods struggle with the randomness and complexity of multiple loads in regional integrated energy systems, resulting in less accurate predictions. To address this problem, this paper proposes a novel two-phase decomposition hybrid forecasting model that combines complementary ensemble empirical mode decomposition, sample entropy, variational mode decomposition, and genetic algorithm-bidirectional long short term memory. The proposed approach begins by decomposing the load sequence into multiple intrinsic mode function components at different frequencies using complementary ensemble empirical mode decomposition. Afterward, the sample entropy values are calculated for each intrinsic mode function. The intrinsic mode function with the highest sample entropy value is subjected to a two-stage decomposition using the variational mode decomposition method. Through this technique, a series of stationary components is acquired. Subsequently, the bidirectional long short term memory model is optimized using the genetic algorithm, and the genetic algorithm-bidirectional long short term memory approach is employed to predict all the decomposed components. Finally, the prediction results are combined and reconstructed to obtain the final prediction value. Experimental results demonstrate the effective handling of non-stationary load sequences by the forecasting model, showcasing the highest level of accuracy in regional integrated energy system multiple loads forecasting.

Synthesis and Photophysical Characterizations of Benzimidazole Functionalized BODIPY Dyes.

Herein, a series of new BODIPY dyes substituted by 2-phenyl benzimidazole units at the meso (C8) position including methyl/ethyl, phenyl, or p-methoxyphenyl moieties at the distal and proximal positions of the BODIPY core have been successfully synthesized and their photophysical characteristics were analyzed. Experimentally investigating absorption and fluorescence profiles in the THF media was followed by density functional theory (DFT) calculations to clarify photophysical features. Theoretical analyses have revealed that upon excitation, both electrons and holes are confined solely within the BODIPY core. The energy levels of the frontier molecular orbitals converge depending on the presence of the phenyl and p-methoxyphenyl substituents. The orbital distributions of both electron and hole were in the -3 and -5 positions, which demonstrates a continuous conjugation with the BODIPY core at these sites. However, the electron density present on the phenyl rings located at the -1, -7, and -8 (meso) positions was found to be negligible. The benzimidazole-BODIPYs exhibited photodynamic activity (Φ∆) ranging from ~ 7% to ~ 11%, determined by a comparative method. Moreover, the compounds have shown to maintain their stability thermally in a non-reactive/inert environment up to temperatures surpassing 300°C, exhibiting primarily a two-phase decomposition process. These compounds have the potential to function as antibacterial and anti-biofilm agents when used in concentrations ranging from 0.5 to 2.0mg/mL. The results provide a basis for evaluating heterocyclic benzimidazole units on photophysical processes containing BODIPY chromophores.

Fabrication and evaluating thermophysical properties of microencapsulated organic and eutectic phase change material

This study explores the synthesis and characterization of microencapsulation of organic and eutectic phase change materials with a melting point ranging from 5 to 30 ℃ for low-temperature applications. Emulsion polymerization utilizing methyl methacrylate monomer as the shell material is employed for effective encapsulation. Comprehensive characterization of microcapsules is undertaken, including chemical composition (using Fourier transform infrared (FT-IR) spectroscopy), morphology (employing scanning electronic microscopy (SEM) and metallurgical microscope), particle size distribution (measured by ImageJ software), enthalpy (evaluated through differential scanning calorimetry (DSC)) and thermal degradation (explored via thermogravimetric analysis (TGA)). The successful production of microencapsulated phase change materials (MPCM) is confirmed by their two-phase decomposition process, the presence of specific chemical bonds, and the spherical morphology of particles with an average diameter between 25.63 and 49.62 μm. However, in the case of eutectic core material, irregular microcapsules are shaped with a larger mean diameter. It has also affected the encapsulation efficiency in which around 29.43% and 30.1% efficiencies are reached for eutectic cores compared to 45.11% and 47% in organic ones. Simultaneously, thermophysical properties of MPCMs, such as density, thermal conductivity, and specific heat, are determined using the displacement method, transient line heat source, transient hot wire, and DSC. These properties are expressed through regression equations based on the determined effective factors. The derived equations enhance the capability to implement MPCMs in practical applications and numerical simulations.

Frequency-based zonal flexible bus design considering order cancellation

Order cancellation is ubiquitous in on-demand mobility pre-booking systems, which affects the pricing and operational strategies of on-demand mobility services. This paper investigates the order cancellation behaviour in a frequency-based flexible bus (FB) system, which provides customized door-to-door service, considering stochastic elastic demand volume and stochastic spatial demand distribution. A two-stage stochastic model that simultaneously optimises the FB service price, FB dispatching, and passenger assignment is developed while capturing the effect of pre-booked order cancellation probability. This cancellation probability is calculated based on the pricing and availability of street-hailing taxi and FB services. The availability of FBs is derived from the underlying stochastic demand and order assignment process, which makes the model nonlinear. To solve the mixed integer nonlinear model, a two-phase decomposition scheme is employed, and then approximation techniques are implemented to make each decomposed problem solvable by commercial solvers. Finally, a gradient decent solution algorithm is applied. Numerical studies are conducted to show the necessity of capturing the impact of order cancellation and illustrate the influence of cancellation penalty, taxi price, and zone division. Finally, a case study with Chengdu data is implemented to examine the performance of our solution algorithm for large-scale optimisation.

Decomposition of Total Organic Halogen Formed during Chlorination: The Iceberg of Halogenated Disinfection Byproducts Was Previously Underestimated.

Total organic halogen (TOX) is widely used as a surrogate bulk parameter to measure the overall exposure of halogenated disinfection byproducts (DBPs) in drinking water. In this study, we surprisingly found that the level of TOX in chlorinated waters had been significantly underestimated under common analytical conditions. After the addition of quenching agent sodium thiosulfate, total organic chlorine and total organic bromine exhibited a two-phase decomposition pattern with increasing contact time, and a significant decomposition was observed for different types of quenching agents, quenching doses, and pH conditions. More importantly, the decomposed TOX closely correlated with the acute toxicity of quenched water against luminous bacteria, implying that the DBPs responsible for TOX decomposition could be of important toxicological significance. Based on nontarget analysis by using high-resolution mass spectrometry, molecular formulas for the decomposed TOX were determined. After re-examining the mass balance of TOX in the context of unintentional decomposition, it was found that both the level and percentage of unknown TOX in chlorinated waters were considerably higher than historically thought. Overall, this study brings new insights into the knowledge of TOX formed during chlorination, providing important clues on the identification of toxicity driver in drinking water.

Robust optimization for the electric vehicle pickup and delivery problem with time windows and uncertain demands

Recently, electric vehicles have become a viable option for city logistics to reduce air pollution and operating noise. In this study we consider the pickup and delivery problem with time windows involving battery-powered electric vehicles under demand uncertainly, where the uncertain demands fall within a budget polytope uncertainty set. We develop a two-stage adaptive robust model to find solutions that are insusceptible to a certain number of deviations in demands, where the routing, and the service start times and remaining battery capacities along a route are fixed before the realization of uncertain demands, while the quantities to load and unload along the route are adjustable to the demand scenario. To solve the model, we develop an exact two-phase decomposition method based on column-and-row generation to alleviate the computational difficulty arising from the large number of demand scenario-related variables and constraints, which decomposes the robust model into a master problem comprising only partial demand scenario-related variables and constraints, and an adversarial separation problem that verifies whether there are infeasible demand scenarios for the solution to the master problem. We develop a tailored branch-and-price-and-cut algorithm incorporating various acceleration strategies to solve the master problem, and a dynamic programming algorithm to solve the adversarial separation problem. We also conduct extensive numerical studies to evaluate the performance of the developed algorithm, ascertain the benefits of the robust model over the deterministic model, and analyze the effect of the charge consumption rate of the battery on the optimal solution.

The Integrated Production-Inventory-Routing Problem with Reverse Logistics and Remanufacturing: A Two-Phase Decomposition Heuristic

Sustainable supply chains depend on three critical decisions: production, inventory management, and distribution with reverse flows. To achieve an effective level of operational performance, policymakers must consider all these decisions, especially in Closed-Loop Supply Chains (CLSCs) with remanufacturing option. In this research paper, we address the Integrated Production-Inventory-Routing Problem with Remanufacturing (IPIRP-R) of returned End-Of-Life (EOL) products. The aim behind solving this optimization problem is to minimize conjointly the total manufacturing, remanufacturing, setup, inventory, and routing costs over the planning horizon. A two-phase decomposition heuristic is developed to solve the model iteratively. Our study finds its originality in the fact of jointly optimizing the Capacitated Lot-Sizing Problem with Remanufacturing (CLSP-R) option and the Vehicle Routing Problem with Simultaneous Pick-up and Delivery (VRPSPD) in a single framework. Numerical results showed that our solution approach provides good solutions regarding small and medium-scale size instances under acceptable computational time, especially for problems occurring with significant manufacturing and remanufacturing costs under relatively low pickup requests.

A two-layer SSA-XGBoost-MLR continuous multi-day peak load forecasting method based on hybrid aggregated two-phase decomposition

To further improve the prediction accuracy under the realization of rolling peak load prediction scenarios for the coming month, an aggregated hybrid two-phase decomposition with two-layer prediction model architecture (ICEEMDAN-SE-VMD-SSA-XBoost-MLR) is proposed in this paper. First, a fully integrated empirical modal decomposition with improved adaptive noise (ICEEMDAN) is used to initially decompose the historical peak load time series, which leads to several eigenmodal functions (IMFs), and by calculating the sample entropy (SE) of each IMF, the IMFs with similar SEs are aggregated and reconstructed into load components representing different time scales. Then, the variational modal decomposition (VMD) is introduced to quadratically decompose the less regular parts of the load components to fully weaken their non-smooth characteristics. Then, extreme gradient boosting (XGBoost) is used to assist in establishing the feature engineering, combining each load component to form the data set required for the prediction model, and using the sparrow search algorithm (SSA) optimized XGBoost with multiple linear regression (MLR) to construct the first layer prediction model for each component, and the prediction results of each component are superimposed to obtain the preliminary prediction results. Finally, inspired by the idea of integrated learning, an error correction link is proposed in this paper to make a secondary prediction of the preliminary prediction results by XGBoost, to better track the fluctuation of load details, realize the error correction of the preliminary prediction results and improve the prediction accuracy.

New double decomposition deep learning methods for river water level forecasting

Forecasting river water levels or streamflow water levels (SWL) is vital to optimising the practical and sustainable use of available water resources. We propose a new deep learning hybrid model for SWL forecasting using convolutional neural networks (CNN), bi-directional long-short term memory (BiLSTM), and ant colony optimisation (ACO) with a two-phase decomposition approach at the 7-day, 14-day, and 28-day forecast horizons. The newly developed CBILSTM method is coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods to extract the most significant features within predictor variables to build a hybrid CVMD-CBiLSTM model. We integrate three distinct datasets (satellite-derived, climate mode indices, and ground-based meteorological observations) to improve the forecasting capability of the CVMD-CBiLSTM model, applied at nineteen different gauging stations in the Australian Murray River system. This proposed model returns a significantly accurate performance with ~98% of all prediction errors within less than ±0.020 m and a low relative root mean square of ~0.08%, demonstrating its superiority over several benchmark models. The results show that using the new hybrid deep learning algorithm with ACO feature selection can significantly improve the accuracy of forecasted river water levels, and therefore, the method is attractive for adopting remote sensing data to the model ground-based river flow for strategic water savings planning initiatives and dealing with climate change-induced extreme events such as drought events.

Substitution-based equipment balancing in service networks with multiple equipment types

Package express companies routinely operate multiple equipment types in their service networks. Nevertheless, the weekly schedule of movements used to transport packages through the network leads to changes in equipment inventory at the facility level, which will hinder their normal operations if it is not carefully taken care of. Traditional rebalancing seeks to move the equipment empty in the most cost efficient way. In contrast, we investigate a substitution-based equipment balancing which has been rarely considered. More specifically, we try to reduce this change, i.e., the equipment imbalance associated with the schedule of movements, by substituting the equipment types initially assigned to movements. Consequently, substantial unnecessary empty repositioning needed to restore balance can be avoided at the end. We conduct complexity analysis of the underlying optimization problems, i.e., finding the minimum imbalance of the network and minimizing the number of substitutions required to achieve the minimum network imbalance. In addition, we develop integer programming (IP) models and propose an efficient two-phase decomposition heuristic for solution. Furthermore, we perform a computational study using real-world instances to analyze the performance of the IP solution approach and assess the benefits of substitution-based equipment balancing.

A Memetic Algorithm for an External Depot Production Routing Problem

This study aims to compare the results of a memetic algorithm with those of the two-phase decomposition heuristic on the external depot production routing problem in a supply chain. We have modified the classical scheme of a genetic algorithm by replacing the mutation operator by three local search algorithms. The first local search consists in exchanging two customers visited the same day. The second consists in trying an exchange between two customers visited at consecutive periods and the third consists in removing a customer from his current tour for a better insertion in any tour of the same period. The tests that were carried out on 128 instances of the literature have highlighted the effectiveness of the memetic algorithm developed in this work compared to the two-phase decomposition heuristic. This is reflected by the fact that the results obtained by the memetic algorithm lead to a reduction in the overall average cost of production, inventory, and transport, ranging from 3.65% to 16.73% with an overall rate of 11.07% with regard to the results obtained with the two-phase decomposition heuristic. The outcomes will be beneficial to researchers and supply chain managers in the choice and development of heuristics and metaheuristics for the resolution of production routing problem.

Spectral Wave Explicit Navier-Stokes Equations for wave-structure interactions using two-phase Computational Fluid Dynamics solvers

This paper proposes an efficient potential and viscous flow decomposition method for wave-structure interaction simulation with single-phase wave models and two-phase Computational Fluid Dynamics (CFD) solvers. The potential part - represents the incident waves - is solved with spectral wave models; the viscous part - represents the complementary perturbation on the incident waves - is solved with the CFD solver. The decomposition strategy is called Spectral Wave Explicit Navier-Stokes Equations (SWENSE), originally proposed for single-phase CFD solvers ( Ferrant et al., 2003). Firstly, this paper presents a new two-phase SWENS Equations with interface capturing technique. To achieve this single-phase and two-phase decomposition, the incident fields are extended in the air with a density-weighted pressure. Secondly, an accurate and efficient interpolation method is proposed to transfer High Order Spectral (HOS) wave model's result on CFD mesh, which reduces drastically the divergence error of the interpolated velocity. Implemented within OpenFOAM, these methods are tested by three verification, validation, and application cases, considering incident wave propagation, high-order loads on a vertical cylinder in regular waves, and a Catenary Anchor Leg Mooring buoy in both regular and irregular waves. Speed-ups between 1.7 and 4.2 are achieved. The wave models and the interpolation method are released open-source to the public.

Air quality data series estimation based on machine learning approaches for urban environments

Air pollution is one of the main environmental problems in residential areas. In many cases, the effects of air pollution on human health can be prevented by forecasting the air quality in the next day. In order to predict the 1 day in advance air quality index (AQI) of Orumiyeh city, the hybrid single decomposition (HSD) and hybrid two-phase decomposition (HTPD) models were used. In the first step, the AQI data were decomposed by complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and was hybridized with general regression neural network (GRNN) and extreme learning machine (ELM) as HSD models. In the second step, using variational mode decomposition (VMD) technique the results of the first intrinsic mode functions (IMFs) of CEEMDAN model were decomposed into nine VMs and were predicted by GRNN and ELM models to obtain IMF1. Finally, in the third step, GRNN and ELM were used again to predict the IMFS as HTPD models. Results showed that in predicting AQI series data by HSD models both CEEMDAN-ELM and CEEMDAN-GRNN models were similarly accurate. Among all the models used, the accuracy of CEEMDAN-VMD-GRNN as the HTPD model was the highest in the training phase (R2 = 0.98, RMSE = 4.13 and MAE = 2.99) and in the testing phase (R2 = 0.74, RMSE = 5.45 and MAE = 3.87). It can be concluded that HTPD models have more accurate results to predict AQI data compared with HSD models.

An intelligent approach for predicting resource usage by combining decomposition techniques with NFTS network

Time sensitive virtual machines that run real-time control tasks are constrained by hard timing requirements. Optimal resource management and guarantee the hard timing requirements of virtual machines are critical goals. Basically, cloud resource usage predicting and resource reservation play a crucial role to achieve these two goals. So, we propose a predicting approach based on two-phase decomposition method and hybrid neural network to predict future resource usage. This paper uses a clustering method based on the AnYa algorithm in an on-line manner in order to obtain the number of fuzzy rules and the initial value of the premise and consequent parameters. Since cloud resource usage varies widely from time to time and server to server, extracting the best time series model for predicting cloud resource usage depend not only on time but on the cloud resource usage trend. For this, we present a recursive hybrid technique based on singular spectrum analysis and adaptively fast ensemble empirical mode decomposition to identify the hidden characteristics of the time series data. This method tries to extract seasonal and irregular components of the time series. According to the simulation results, it can be found that the proposed model can have significantly better performance than the three comparison models from one-step to six-step CPU usage predictions with the MAPE of 33.83% average performance promotion, MAE of 36.54% average performance promotion, RMSE of 36.70% average performance promotion.

A novel complex manufacturing business process decomposition approach in cloud manufacturing

In cloud manufacturing, manufacturing business processes are virtualized and encapsulated into manufacturing services. Enterprises can customize their manufacturing business applications on cloud manufacturing platform. To facilitate the customization, a global manufacturing business process is generally built and transformed into a feature model. However, since most global manufacturing business processes have a large number of nodes and complex dependency relationships, it is difficult to transform them into feature models. Therefore, we propose a novel complex manufacturing business process decomposition approach in cloud manufacturing. The approach is beneficial to construct a desired feature model. Firstly, combined with the domain engineering theory, the complex manufacturing business process customization approach in cloud manufacturing is elaborated. Secondly, the complex manufacturing business process decomposition approach is proposed. A global manufacturing business process network (GMBPN) is constructed according to the input and output relationships of manufacturing business activities. Based on the GMBPN, a two-phase decomposition algorithm is presented. In the community detection phase, the nodes in the GMBPN with functional similarity are clustered into the same community with an adaptive strategy. In the sub-process reconstruction phase, the corresponding sub-process is reconstructed in each community. We evaluate the decomposition algorithm with an actual elevator design business process. Experimental results show the proposed algorithm is effective to find the optimal decomposition results.

A Hybrid Forecasting Model for Short-Term Power Load Based on Sample Entropy, Two-Phase Decomposition and Whale Algorithm Optimized Support Vector Regression

To improve the accuracy and reliability of short-term power load forecasting and reduce the difficulty caused by load volatility and non-linearity, a hybrid forecasting model (CEEMDAN-SE-VMD-PSR-WOA-SVR) is proposed. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to generate multiple intrinsic modal functions (IMF) by decomposing the historical power load series. Then the sample entropy (SE) of each IMF is calculated to quantitatively evaluate the corresponding complexity. Afterward, variational mode decomposition (VMD) is adopted to achieve secondary decomposition for the component with the maximum sample entropy. Subsequently, the phase space reconstruction (PSR) is applied to reconstruct each IMF into a high-dimensional feature space matrix, which is formed as the input of support vector regression (SVR). Finally, SVR optimized by whale optimization algorithm (WOA) is used for the prediction, where the predicted values of all IMFs are accumulated to obtain the final prediction results. The experimental result demonstrates that the proposed hybrid model can effectively decompose the load series with non-linear characteristic and provide more accurate forecasting results by comparing the other models.

Short-Term Load Dispatching Method for a Diversion Hydropower Plant with Multiple Turbines in One Tunnel Using a Two-Stage Model

Short-term load dispatching (STLD) for a hydropower plant with multiple turbines in one tunnel (HPMTT) refers to determining when to startup or shutdown the units of different tunnels and scheduling the online units of each tunnel to obtain optimal load dispatch while simultaneously meeting the hydraulic and electric system constraints. Modeling and solving the STLD for a HPMTT is extremely difficult due to mutual interference between units and complications of the hydraulic head calculation. Considering the complexity of the hydraulic connections between multiple power units in one tunnel, a two-phase decomposition approach for subproblems of unit-commit (UC) and optimal load dispatch (OLD) is described and a two-stage model (TSM) is adopted in this paper. In the first stage, an on/off model for the units considering duration constraints is established, and the on/off status of the units and tunnels is determined using a heuristic searching method and a progressive optimal algorithm. In the second stage, a load distribution model is established and solved using dynamic programming for optimal load distribution under the premise of the on/off status of the tunnel and units in the first stage. The proposed method is verified using the load distribution problem for the Tianshengqiao-II reservoir (TSQII) in dry season under different typical load rates. The results meet the practical operation requirements and demonstrate the practicability of the proposed method.

A novel heuristic algorithm with activity back-shift response model for resource-constrained project scheduling problem

The resource-constrained project scheduling problem is one of the most intractable problems in the field of operation research and management science. It is important for both academic research and engineering applications to develop effective solution algorithms for scheduling problems. In this paper, the activity back-shift response model is introduced for scheduling problems, based on which a resource-constrained scheduling model is built, and a two-phase decomposition algorithm is proposed to address the project resource-constraint and timing optimization. The first stage is resource optimization, where the conflicted activities are resolved by a genetic algorithm. In this case, if parallel activities are scrambling resources during resource optimization, the activity priority, determined by comparing the activity back-shift response time, is applied to dissolve the resource conflict. The second stage is timing optimization. After obtaining the progress plan with optimized resource constraints, activities are adjusted to move forward as much as possible. The validity of the proposed algorithm is verified by a real project case. The computational results reveal that the proposed model and algorithm obtain appropriate results and have the potential to be applied to other similar problems.

Two-phase decomposition method for the last train departure time choice in subway networks

An urban subway network with a number of service lines forms the backbone of the public transport system for a large city of high population, such as Singapore, Hong Kong and Beijing. Passengers in these large cities heavily rely on urban subway networks for their daily life. The departure times of the last trains running on different lines of an urban subway network should be well coordinated in order to serve more passengers who can successfully transfer from one line to another, which is referred to as the last train departure time choice problem. This study aims to develop a global optimization method that can solve the last train departure time choice problem for large-scale urban subway networks. To do so, it first formulates a mixed-integer linear programming (MILP) model by introducing auxiliary binary and integer decision variables. For the real-life and large-scale instances, however, the formulated MILP model cannot be solved directly by the global optimization methods such as branch-and-bound algorithm invoked by CPLEX – one of the powerful optimization solvers because of the instance sizes. An effective two-phase decomposition method is thus proposed to globally solve the large-scale problems by decomposing the original MILP into two MILP models with small sizes. Finally, a real case study from the Beijing subway network is conducted to assess the efficiency and applicability of the two-phase decomposition method and perform the necessary sensitivity analysis of the operational parameters involved in the last train departure time choice problem.

Tea polyphenols dominate the short-term tea (Camellia sinensis) leaf litter decomposition.

Polyphenols are one of the most important secondary metabolites, and affect the decomposition of litter and soil organic matter. This study aims to monitor the mass loss rate of tea leaf litter and nutrient release pattern, and investigate the role of tea polyphenols played in this process. High-performance liquid chromatography (HPLC) and classical litter bag method were used to simulate the decomposition process of tea leaf litter and track the changes occurring in major polyphenols over eight months. The release patterns of nitrogen, potassium, calcium, and magnesium were also determined. The decomposition pattern of tea leaf litter could be described by a two-phase decomposition model, and the polyphenol/N ratio effectively regulated the degradation process. Most of the catechins decreased dramatically within two months; gallic acid (GA), catechin gallate (CG), and gallocatechin (GC) were faintly detected, while others were outside the detection limits by the end of the experiment. These results demonstrated that tea polyphenols transformed quickly and catechins had an effect on the individual conversion rate. The nutrient release pattern was different from other plants which might be due to the existence of tea polyphenols.