Steam consumption prediction of a gas sweetening process with methyldiethanolamine solvent using machine learning approaches

This study proposes a comprehensive data processing and modeling framework for building high‐accuracy machine learning model to predict the steam consumption of a gas sweetening process. The data pipeline processes raw historical data of this application and identifies the minimum number of modeling variables required for this prediction in order to ease the applicability and practicality of such methods in the industrial units. On the modeling end, an empirical comparison of most of the state‐of‐the‐arts regression algorithms was run in order to find the best fit to this specific case study. The ultimate goal is to leverage this model to identify the achievable energy conservation opportunity in such plants. The historical data for this modeling was collected from a gas treating plant at South Pars Gas Complex for 3 years from 2017 to 2019. This data gets passed through a multistage data processing scheme that conducts multicollinearity analysis and model‐based feature selection. For model selection, a wide range of regression algorithms from different classes of regressor have been considered. Among all these methods, the Gradient Boosting Machines model outperformed the others and achieved the lowest cross‐validation error. The results show that this model can predict the steam consumption values with 98% R‐squared accuracy on the holdout test set. Furthermore, the offline analysis demonstrates that there is a potential of 2% energy saving, equivalent to 24 000 metric tons of annual steam consumption reduction, which can be achieved by mapping the underperforming energy consumption states of the unit to the expected performances predicted by the model.     

基于能耗监管平台的现有医院空调系统运行节能优化

基于上海某医院能耗监管平台数据,利用EnergyPlus软件建立了该医院门诊楼建筑模型和空调系统模型,并验证了该模型的准确性。对该医院集中式空调系统运行策略进行优化分析,结果表明：当室内负荷低于冷水机组总额定制冷量80%时,负荷分配优化运行方案节能率最高,达到9.7%;当室内负荷高于冷水机组总额定制冷量80%时,机组联合运行并采用负荷平均分配时比一台机组满负荷运行另一台机组部分负荷运行时节能,节能率为1.5%～3.7%。分析了冷却水变流量对冷水机组和冷却水系统的影响及节能效果。结果表明,离心机组变流量运行时节能率达到17%,而螺杆机组在定流量45.13 kg·s⁻¹运行时比较合理和节能。     

热泵系统输送能耗比例及系统优化

摘 要：利用热泵技术为建筑物供暖制冷,具有巨大的经济与环保效益。为了降低热泵系统输送能耗,建立了输送距离界限模型和输送能耗比例模型。综合考虑系统的运行费与初投资,在管材价格、安装价格一定的条件下,对传统比摩阻计算式进行了经济优化,得到了适用于热泵系统的经济比摩阻范围（40~80Pa/m）,该值较传统经济比摩阻取值偏小,以此经济比摩阻可确定水源输送距离界限（1200~2500m）,并据此指导输配管网设计,以降低和控制热泵系统输送能耗比例(10%~20%)。     

Modeling and daily operation optimization of a distributed energy system considering economic and energy aspects

This paper presents a distributed energy system (DES) for a local district and formulates a constrained nonlinear multiobjective optimization model for the daily operation of the system. The main objective of the study is to increase the efficiency by minimizing energy cost, energy consumption, and energy losses. It is implemented through the integration and complementation of renewable energies and fossil fuels as well as the recycling utilization of waste heat in the DES. The consideration of network topology and energy losses of water heating network could also contribute to the improvement of energy efficiency. To solve the optimization problem, a novel Whale Optimization Algorithm is employed. Furthermore, the economic and energy performance of the DES are evaluated and compared with that of conventional centralized energy systems, ie, the EG and MG energy‐supply modes. After simulation studies, the hourly optimal energy (both natural gas and electricity) purchasing schedule as well as the hourly optimal set points of mass water flow rates and supply/return water temperatures could be determined. The results show that the DES saves more than 50% of energy costs/energy consumption than the MG mode and over 22% than the EG mode for a whole day, verifying the competitive advantage and great potential of both energy saving and cost reduction of the DES.     

中密度纤维板热压过程能耗优化系统的研究

利用现有环境试验数据并加以整理,在Windows操作系统环境下,采用SQL Server数据库管理系统,设计和创建了基于C/S结构中密度纤维板热压过程工艺参数数据库系统.该系统主要包括网络数据库管理系统和查询系统两大部分,易于扩展,功能强大,可实现局域网/广域网上的数据共享.并且针对工艺参数数据库采用BP神经网络优化算...     

Energy consumption in greenhouses and selection of an optimized heating system with minimum energy consumption

In this paper, heat loss is precisely computed by a proposed code considering different climates. Estimating the costs of different central heating system, unit heaters were selected as the most feasible system. Finally, considering the heating capacity and unit heater's dimensions a computational fluid dynamics model was developed to find the optimized configuration of unit heaters in a typical greenhouse. Using this model, the required thermal load for a greenhouse based on the daily temperature of Arak city in 2017 was computed with a smart control system. It was found that savings in energy consumption were approximately 5447 m³ of natural gas each year.     

Comprehensive evaluation of household indirect energy consumption and impacts of alternative energy policies in China by input–output analysis

Households consume a large amount of indirect energy through the consumption of goods and services. This fact makes the quantitative analysis of indirect household energy consumption the foundation of energy policy design. This paper improves the compilation method of energy input–output tables, and establishes a sequence of energy input–output tables for China. Based on these tables, the indirect energy consumption of both rural and urban households is calculated. Then, with economic data for the year of 2005, the adjusted input–output price model is applied to evaluate how the alternative energy policies impact production prices, consumption prices, and real income of rural and urban households through the mechanism of indirect energy consumption by using electricity as an example. This research has practical implications for Chinese economy. The integration of energy-efficiency improvements and energy prices increase serves as a means to achieve both economic and energy conservation goals, and may also have a positive effect on residents’ real income and a minimal effect on production prices.     

New optimized model for water temperature calculation of river-water source heat pump and its application in simulation of energy consumption

The energy consumption calculation plays an important role in the analysis of project economic and social benefits. In order to calculate energy consumption accurately, this research presents a water temperature of condenser inlet calculation model of river-water source heat pump unit. The feasibility and calculation error of the model had been analyzed. Additionally, the new water temperature calculation model had been validated via an engineering case. The results showed that the hourly water temperature in 24 h could be replaced by daily average water temperature due to little change of the daily water temperature change. In this case, the calculation error could be less than 5%. It is found that despite water temperature has many influenced factors, there is a remarkable relationship between the daily average water temperature and daily average outdoor dry bulb temperature by data analysis (R² ≈ 0.9). The influence of river sampling location on water temperature calculation of condenser inlet could be ignored due to slight temperature changes (within 0.15 °C). The method proposed in this paper met the engineering accuracy and provided a very effective method for the engineering calculation of energy consumption of water chilling unit.     

绿色饭店能源消耗评估指标体系的研究与应用

以调查取样的数据为基础，参照国外先进水平，通过对关键指标的计算和影响因素的统计分析，构建了一个量化的饭店经营能耗指标体系和评估模型，用以指导饭店的能耗管理。在此基础上，通过评估模型的应用．可以评价一个饭店能耗使用的等级，为饭店能耗管理提供了量化管理的规范，以产生良好的节能效益。     

Comparison of neural network,conditional demand analysis,and engineering approaches for modeling end-use energy consumption in the residential sector

This paper investigates the use of conditional demand analysis (CDA) method to model the residential end-use energy consumption at the national level. There are several studies where CDA was used to model energy consumption at the regional level; however the CDA method had not been used to model residential energy consumption at the national level. The prediction performance and the ability to characterize the residential end-use energy consumption of the CDA model are compared with those of a neural network (NN) and an engineering based model developed earlier. The comparison of the predictions of the models indicates that CDA is capable of accurately predicting the energy consumption in the residential sector as well as the other two models. The effects of socio-economic factors are estimated using the NN and the CDA models, where possible. Due to the limited number of variables the CDA model can accommodate, its capability to evaluate these effects is found to be lower than the NN model.     

Overview of the use of artificial neural networks for energy‐related applications in the building sector

The incessant growing of the world's energy consumption and associated greenhouse gases emissions have created tremendous problems to be solved by today's and future generations. As the building sector is one of the biggest energy consumers, reducing its energy consumption is now mandatory. Being able to conceive and built efficient buildings, to effectively manage and operate them, and to rapidly renovate the existing building stock is a challenging task. Neural networks models open new possibilities to address this problem. This paper offers a comprehensive review of the studies that use neural networks for energy‐related applications in the building sector focusing on their application and on the technical characteristics of the network (ie, learning algorithm, number of layers, number of neurons, inputs and output variables, and performance criteria). On the basis of this review, limitations concerning the use of neural networks in the building sector along with existing research gaps and future research directions are identified.     

Optimal sizing of autonomous hybrid energy system using supply‐demand‐based optimization algorithm

Hybrid energy systems (HESs) comprising photovoltaic (PV) arrays and wind turbines (WTs) are remarkable solutions for electrifying remote areas. These areas commonly fulfil their energy demands by means of a diesel genset (DGS). In the present study, a novel computational intelligence algorithm called supply‐demand‐based optimization (SDO) is applied to the HES sizing problem based on long‐term cost analysis. The effectiveness of SDO is investigated, and its performance is compared with that of the genetic algorithm (GA), particle swarm optimization (PSO), gray wolf optimizer (GWO), grasshopper optimization algorithm (GOA), flower pollination algorithm (FPA), and big‐bang‐big‐crunch (BBBC) algorithm. Three HES scenarios are implemented using measured solar radiation, wind speed, and load profile data to electrify an isolated village located in the northern region of Saudi Arabia. The optimal design is evaluated on the basis of technical (loss of power supply probability [LPSP]) and economic (annualized system cost [ASC]) criteria. The evaluation addresses two performance indicators: surplus energy and the renewable energy fraction (REF). The results reveal the validity and superiority of SDO in determining the optimal sizing of an HES with a higher convergence rate, lower ASC, lower LPSP, and higher REF than that of the GA, PSO, GWO, GOA, FPA, and BBBC algorithms. The performance analysis also reveals that an HES comprising PV arrays, WTs, battery banks, and DGS provides the best results: 238.7 kW from PV arrays, 231.6 kW from WTs, 192.5 kWh from battery banks, and 267.6 kW from the DGS. The optimal HES exhibits a high REF (66.4%) and is economically feasible ($104 323.10/year) and environmentally friendly. The entire load demand of the area under study is met without power loss (LPSP = 0%).     

Effect of cascade storage system topology on the cooling energy consumption in fueling stations for hydrogen vehicles

One of the main obstacles of the diffusion of fuel cell electric vehicles (FCEV) is the refueling system. The new stations follow the refueling protocol from the Society of Automotive Engineers where the way to reach the target pressure is not explained. This work analyzes the thermodynamics of a hydrogen fueling station in order to study the effects of the cascade storage system topology on the energy consumption for the cooling facility. It is found that the energy consumption for cooling increases, expanding the total volume of the cascade storage system. Comparing the optimal and the worst volume configurations of the cascade storage tanks at different ambient temperatures, the energy saving is approximately 12% when the average ambient temperature is 20 °C and around 20% when the average ambient temperature is 30 °C. The energy consumption for cooling is significantly influenced by the topology of the cascade storage system and it is particularly relevant in the case of low daily-dispensed amount of hydrogen.     

考虑电转氢气过程及综合需求响应的电-氢-气综合能源系统协调优化运行

文章介绍了电转氢气、氢气转天然气的过程,并在此基础上构建了电-氢-气综合能源系统。通过考虑综合需求侧响应,建立以最小化系统运行总成本为目标函数的协调优化模型,利用禁忌搜索算法和粒子群算法相结合的改进算法进行求解。经验证,考虑电转氢气过程及综合需求响应的协调优化方案增强了综合能源系统的经济性、环保性,提高了新能源的消纳能力。     

Internet of things and cloud computing‐based energy management system for demand side management in smart grid

A smart grid is an electricity network, which deals with electronic power conditioning and control of production, transmission, and distribution of electrical power by employing digital communication technologies to monitor and manage local changes in electricity usage. In the traditional power grid, energy consumers remain oblivious to their power consumption patterns, resulting in wasted energy as well as money. This issue is severely pronounced in the developing countries where there is a huge gap between demand and supply, resulting in frequent power outages and load‐shedding. For electrical energy savings, the smart grid employs demand side management (DSM), which refers to adaptation in consumer's demand for energy through various approaches such as financial incentives and awareness. The DSM in future smart grid must exploit automated energy management systems (EMS) built upon the state‐of‐the‐art technologies such as the internet of things (IoT) and cloud and/or fog computing. In this paper, we present the architecture framework, design, and implementation of an IoT and cloud computing‐based EMS, which generates load profile of consumer to be accessed remotely by utility company or by the consumer. The consumers' load profiles enable utility companies to regulate and disseminate their incentives and incite the consumers to adapt their energy consumption. Our designed EMS is implemented on a Project Circuit Board (PCB) to be easily installed at the consumer premises where it performs the following tasks: (a) monitors energy consumption of electrical appliances by means of our designed current and voltage sensors, (b) uploads sensed data to Google Firebase cloud over many‐to‐many IoT communication protocol Message Queuing Telemetry Transport (MQTT) where consumer's load profile is generated, which can be accessed via a web portal. These load profiles serve as input for implementing the various DSM approaches. Our results demonstrate generated load profiles of consumer load in terms of current, voltage, energy, and power accessible via a web portal.     

工业企业用能系统的优化与流程再造

借助能源审计方法,系统分析企业能耗构成及原用能系统存在的问题。以维生素C产品为重点,从系统角度进行用能系统的过程集成与优化,再造新的用能流程,并适时地通过过程系统节能技术、经验向其他产品的推广和平移,实现整个制药企业的系统节能。     

Mapping of solar energy potential in Indonesia using artificial neural network and geographical information system

The first objective of this study is to determine the theoretical potential of solar irradiation in Indonesia by using artificial neural networks (ANNs) method. The second objective is to visualize the solar irradiation by province as solar map for the entire of Indonesia. The geographical and meteorological data of 25 locations that were obtained from NASA database are used for training the neural networks and the data from 5 locations were used for testing the estimated values. The testing data were not used in the training of the network in order to give an indication of the performance of the system at unknown locations. In this study, the multi layer perceptron ANNs model, with 9 inputs variables i.e. average temperature, average relative humidity, average sunshine duration, average wind speed, average precipitation, longitude, latitude, latitude, and month of the year were proposed to estimate the monthly solar irradiation as the output. Statistical error analysis in terms of mean absolute percentage error (MAPE) was conducted for testing data to evaluate the performance of ANN model. The best result of MAPE was found to be 3.4% when 9 neurons were set up in the hidden layer. As developing country and wide islands area, Indonesia has the limitation on the number of meteorological station to record the solar irradiation availability; this study shows the ANN method can be an alternative option to estimate solar irradiation data. Monthly solar mapping by province for the entire of Indonesia are developed in GIS environment by putting the location and solar irradiation value in polygon format. Solar irradiation map can provide useful information about the profile of solar energy resource as the input for the solar energy system implementation.     

不同地区住宅能耗与太阳能吸收式空调系统匹配性分析与模拟研究

为了满足农村住宅清洁用能的需求,多种形式的能源系统逐渐开始应用于广大的农村地区。随着太阳能集热器集热效率的提高,热驱动机组各项性能不断改善,这样有利于太阳能吸收式空调系统在农村地区的应用。为了研究太阳能吸收式空调系统与农村住宅全年能耗的匹配问题,文章首先建立了DeST住宅模型,然后利用TRNSYS软件建立了太阳能吸收式空调系统模型,最后根据模拟结果对国内不同气候区内农村住宅供热季、供冷季的平均热负荷值,以及全年的能耗进行分析。此外,文章还分析了典型日太阳能吸收式空调系统的运行策略与效果。分析结果表明:在无辅助热源的条件下,太阳能集热器的集热温度会大于80℃,满足空调机组的热驱动温度,因此可以作为太阳能吸收式空调系统的的热源;当启动温度为85℃时,空调机组的制冷量可以达到8 kW,性能系数COP为0.733。     

Modeling,design, and optimization of a cost‐effective and reliable hybrid renewable energy system integrated with desalination using the division algorithm

People in the Middle East are facing the problem of freshwater shortages. This problem is more intense for a remote region, which has no access to the power grid. The use of seawater desalination technology integrated with the generated energy unit by renewable energy sources could help overcome this problem. In this study, we refer a seawater reverse osmosis desalination (SWROD) plant with a capacity of 1.5 m³/h used on Larak Island, Iran. Moreover, for producing fresh water and meet the load demand of the SWROD plant, three different stand‐alone hybrid renewable energy systems (SAHRES), namely wind turbine (WT)/photovoltaic (PV)/battery bank storage (BBS), PV/BBS, and WT/BBS are modeled and investigated. The optimization problem was coded in MATLAB software. Furthermore, the optimized results were obtained by the division algorithm (DA). The DA has been developed to solve the sizing problem of three SAHRES configurations by considering the object function's constraints. These results show that this improved algorithm has been simpler, more precise, faster, and more flexible than a genetic algorithm (GA) in solving problems. Moreover, the minimum total life cycle cost (TLCC = 243 763$), with minimum loss of power supply probability (LPSP = 0%) and maximum reliability, was related to the WT/PV/BBS configuration. WT/PV/BBS is also the best configuration to use less battery as a backup unit (69 units). The batteries in this configuration have a longer life cycle (maximum average of annual battery charge level) than two other configurations (93.86%). Moreover, the optimized results have shown that utilizing the configuration of WT/PV/BBS could lead to attaining a cost‐effective and green (without environmental pollution) SAHRES, with high reliability for remote areas, with appropriate potential of wind and solar irradiance.     

Energy performance of a hybrid space-cooling system in an office building using SSPCM thermal storage and night ventilation

Thermal performance of a hybrid space-cooling system with night ventilation and thermal storage using shape-stabilized phase change material (SSPCM) is investigated numerically. A south-facing room of an office building in Beijing is analyzed, which includes SSPCM plates as the inner linings of walls and the ceiling. Natural cool energy is charged to SSPCM plates by night ventilation with air change per hour (ACH) of 40 h⁻¹ and is discharged to room environment during daytime. Additional cool-supply is provided by an active system during office hours (8:00-18:00) necessary to keep the maximum indoor air temperature below 28 °C. Unsteady simulation is carried out using a verified enthalpy model, with a time period covering the whole summer season. The results indicate that the thermal-storage effect of SSPCM plates combined with night ventilation could improve the indoor thermal-comfort level and save 76% of daytime cooling energy consumption (compared with the case without SSPCM and night ventilation) in summer in Beijing. The electrical COPs of night ventilation (the reduced cooling energy divided by fan power) are 7.5 and 6.5 for cases with and without SSPCM, respectively.