Optimal Design of Energy System Based on the Forecasting Data with Particle Swarm Optimization

Renewable energy source has developed rapidly and attracted considerable attention. The integration of renewable energy into the energy supply chain requires precise forecast of the output of energy supply chain, thereby reducing energy resource waste and greenhouse gas emissions. In this study, a coupled model system is developed to forecast energy supply chain for the design optimization of distributed energy system, which can be divided into two parts. In the first part, long short-term memory (LSTM) and particle swarm optimization algorithm (PSO) contribute to energy supply chain forecast considering time series, and particle swarm optimization is used to optimize the parameters of the long short-term memory model to improve the forecast accuracy. Results show that the mean absolute error and root mean squared error are 8.7 and 16.3 for the PSO-LSTM model, respectively. In the second part, the forecast results are used as input of the distributed energy system to further optimize the design and operation schemes, so as to achieve the coupling optimization of forecast and design. Finally, a case study is carried out to verify the effectiveness of the proposed method

A New Software for Management, Scheduling, and Optimization for the Light Hydrocarbon Pipeline Network System of Daqing Oilfield

This paper presents the new software which specifically developed based on Visual Studio 2010 for Daqing Oilfield China includes the most complex light hydrocarbon pipeline network system in Asia, has become a powerful auxiliary tool to manage field data, makes scheduling plans for batching operation, and optimizes pumping plans. Firstly, DMM for recording and managing field data is summarized. Then, the batch scheduling simulation module called SSM for the difficult batch-scheduling issues of the multiple-source pipeline network system is introduced. Finally, SOM, that is Scheduling Optimization Module, is indicated for solving the problem of the pumps being started up/shut-down frequently

Gas-Liquid Stratified Flow in Pipeline with Phase Change

When the natural gas with vapor is flowing in production pipeline, condensation occurs and leads to serious problems such as condensed liquid accumulation, pressure and flow rate fluctuations, and pipeline blockage. This chapter aims at studying phase change of vapor and liquid-level change during the condensing process of water-bearing natural gas characterized by coupled hydrothermal transition and phase change process. A hydrothermal mass transfer coupling model is established. The bipolar coordinate system is utilized to obtain a rectangular calculation domain. An adaptive meshing method is developed to automatically refine the grid near the gas-liquid interface. During phase change process, the temperature drop along the pipe leads to the reduction of gas mass flow rate and the rise of liquid level, which results in further pressure drop. Latent heat is released during the vapor condensing process which slows down the temperature drop. Larger temperature drop results in bigger liquid holdup while larger pressure drop causes smaller liquid holdup. The value of velocity with phase change is smaller than that without phase change while the temperature with phase change is bigger. The highest temperature locates in gas phase. But near the pipe wall the temperature of liquid region is higher than gas region

Simultaneously Retrofit of Heat Exchanger Networks and Towers for a Natural Gas Purification Plant

As an essential part of Heat Integration, the heat exchanger network (HEN) plays a vital role in large-scale industrial fields. The optimisation of HEN can increase energy efficiency and considerably save the operating and investment cost of the project. This study presents a novel approach for simultaneous optimisation of plant operating variables and the HEN structure of an existing natural gas purification process. The objective function is the total energy consumption of the studied process. A two-stage method is developed for optimisation. In the first stage, a particle swarm optimisation (PSO) algorithm is developed to optimise variables including tower top pressure, tower bottom pressure, and reflux ratio on the HEN, thereby changing the initial temperatures of cold and hot streams in the HEN. In the second stage, a shifted retrofit thermodynamic grid diagram (SRTGD)-based model and the corresponding solving algorithm was applied to retrofit the HEN. The case study shows that the optimal operating conditions of towers and temperature spans of heat exchangers can be solved by the proposed method to reduce the total energy consumption. The case study shows that the total energy consumption is reduced by 41.5 %

Optimal Planning for Deepwater Oilfield Development Under Uncertainties of Crude Oil Price and Reservoir

The development planning of deepwater oilfield directly influences production costs and benefits. However, the uncertainties of crude oil price and reservoir and the special production requirements make it difficult to optimize development planning of deepwater oilfield. Although there have been a number of scholars researching on this issue, previous models just focused on several special working conditions and few have considered energy supply of floating production storage and offloading (FPSO). In light of the normal deepwater production development cycles, in this paper, a multiscenario mixed integer linear programming (MS-MILP) method is proposed based on reservoir numerical simulation, considering the uncertainties of reservoir and crude oil price and the constraint of energy consumption of FPSO, to obtain the globally optimal development planning of deepwater oilfield. Finally, a real example is taken as the study objective. Compared with previous researches, the method proposed in this paper is testified to be practical and reliable

Optimization of regional methanol multimodal transport system planning considering refined oil storage and transportation facilities

ObjectiveThe geographical disparity between methanol production and consumption, compounded by the limited capacity of current storage and transportation facilities, hinders large-scale, long-distance, low-carbon, and efficient methanol transport. Concurrently, the ongoing decrease in refined oil consumption has led to significant overcapacity in existing refined oil storage and transportation facilities. Therefore, when establishing an optimized methanol storage and transportation network, it is essential to comprehensively consider the integration and reutilization of existing refined oil storage and transportation facilities to maximize resource sharing and economic efficiency. MethodsTo address this, a framework for planning a regional methanol multimodal transport system was developed, encompassing supply, transportation, and demand. Multiple transport modes, namely road tankers, rail tankers, methanol pipelines, and refined oil pipelines, were considered comprehensively. Three methanol multimodal-transport scenarios were defined: current methanol supply-demand capacity (Scenario 1), 2030 methanol supply-demand capacity (Scenario 2), and 2030 methanol supply-demand capacity with increased refined-oil surplus capacity (Scenario 3). On this basis, an optimization model for the regional multimodal transport of methanol, suitable for the collaborative optimization of multiple routes and modes, was established. The objective function was to minimize the total cost of methanol transportation, construction of methanol pipelines, transformation of refined oil pipelines, construction of methanol storage tanks, and transformation of refined oil storage tanks. Constraints such as methanol material balance, construction of methanol pipelines, and transformation of refined oil pipelines were incorporated to conduct an optimization analysis of the layout of methanol storage and transportation facilities. ResultsThe proposed model was applied to a regional multimodal transport system in China, significantly reducing the storage and transportation costs of methanol by integrating multiple modes of transportation including refined oil pipelines. In Scenario 1, the main refined oil pipelines I, II, and III were all repurposed for methanol transport, resulting in an overall load rate increase of 27％–47％ compared to their exclusive use for refined oil transport. ConclusionThe proposed planning framework and optimization model can provide effective technical paths and decision-making support for the planning of regional methanol storage and transportation facilities in China. Incorporating the surplus capacity of refined oil pipelines into the methanol multimodal transport system is of great practical significance for promoting the coordinated development of regional energy

Prediction of mixed oil concentration in product oil pipeline coupling oil mixing mechanism and data correction

Objective Batch pipelining of product oils inevitably leads to oil mixing at the junction of oils transmitted in sequential batches within the pipelines. This disruption affects the formulation of station dispatching plans and offtake schemes. Therefore, accurately predicting the concentration distribution of oil mixing sections is considered a crucial foundation for enhancing the control of oil mixing, reducing energy consumption for mixed oil treatment, and preventing oil quality incidents at stations. Despite this, multi-dimensional numerical models of oil mixing have exhibited several drawbacks. These include lengthy calculation processes, inefficiencies in long-distance pipeline applications, neglect of the oil mixing progression mechanism process due to traditional data-driven approaches, and violations of physical principles, low accuracy, and poor interpretability in their results. Methods Through analyzing the oil mixing progression mechanism, this study delved into the fundamental control equations and initial boundary conditions relevant to oil mixing progression. A physics-guided coupling loss function was constructed by coupling the automatic differential method with a deep learning model, enabling the confinement of model prediction results within the corresponding physical solution space of oil mixing progression. Following this, the initial numerical solutions for short pipeline sections were used to predict the mixed oil concentration distribution throughout long-distance transmission pipelines based on the recursive rolling prediction and data correction methods. Results The numerical examples demonstrated a higher accuracy of the established model than traditional data-driven methods, manifesting a notable 91% decrease in MAPE. This model also displayed reduced dependency on data and alleviated Root Mean Square Error (RMSE) fluctuations by 60% with changes in data size. Moreover, the computational expenses were minimized to a mere 12% of those incurred by the Fluent numerical simulation method. In practical engineering applications, MAE of the suggested framework substantially decreased by 71% and 58% respectively when compared to the Taylor model and the enhanced Taylor model. These findings underscored the effectiveness of the proposed prediction method in resolving the mixed oil concentration distribution in long-distance pipelines. Conclusion The proposed prediction method of mixed oil concentration in product oil pipelines couples oil mixing progression mechanism constraints with data correction. This method accurately and efficiently predicts mixed oil concentration distribution in long-distance pipelines, guiding the formulation of mixed oil receiving plans for stations and enhancing the intelligent control of oil mixing