Role of Heating Condition in Polyethylene Behaviors under Nitrogen and Air Atmosphere

Recycle of plastic waste is an indisputable means to alleviate both environment and energy crisis.In this work,effects of heating condition on polyethylene behaviors in nitrogen and air were studied.It was observed that polyethylene behavior was a single step in nitrogen,while the multiple steps occurred in air.According to the weight loss and heat flow curves,polyethylene behaviors in air were divided into three regions:low-temperature(<340 ℃),middle-temperature(340 – 380 ℃) and high-temperature(> 380 ℃) regions.Kinetic analysis revealed that the partial oxidization took the dominance in the low-temperature region,which seldom formed CO and CO_(2);the combustion took the dominance in the middle-temperature region,which was positive to the formation of CO and CO_(2);the pyrolysis was initiated and enhanced in the high-temperature region,which inhibited the formation of CO and CO;.According to the kinetic simulation,a synergistic effect between the pyrolysis and combustion was proposed to account for the acceleration of polyethylene conversion.This work may provide useful information about polyethylene behaviors under heating condition,and help to design and optimize plastic waste incineration process.

THE EXERGETIC EFFICIENCY OF MSF PROCESS AND THE CONDITIONS OF DESALINATION BY WASTE HEAT

The exergy losses and thermodynamic efficiency of MSF plant with brine recirculation are discussed bymeans of temperature difference functions proposed by the auther.In a MSF plant,the irreversible losses are found mainly in irreversible heat-transfer and flash evaporationprocesses.However,the basic variables are the temperature drop from stage to stage and the temperaturedifferences between flashed vapor and cooling water.In this paper,the flash temperature difference func-tion,the heat transfer temperature difference function and the total temperature difference function are sug-gested.The proposed temperature difference functions of MSF plant provide a convenient tool to analyse theirreversible behavior and evaluate the exergetic efficiency of this system,because without such improvement thecalculation of the exergetic efficiency of a MSF plant according to the classical formula will be not onlyinconvenient but also insignificant.As a result of present analysis,the reasonable parameters based on theenergy consumption are easily chosen.The above-mentioned principles are confirmed by commercial plants and a pilot plant in Tianjin.

Damage Action of Alkali-resistant Glass Fiber in Cement-based Material

The compressive strength and flexural strength with the same strength class cement mortar of the alkali-resistant glass fiber cement mortar were tested in standard and hot-water curing condition, and the damage mechanism of alkali-resistant glass fiber was studied. The interaction mechanisms of the chemical erosion and physical injury in different curing conditions were studied in order to summarize the damage mechanism of alkali-resistant glass fiber in cement-based materials, and chloride diffusivity coefficient and porosity of cement mortar were tested in the different curing conditions. The experimental results are that the strength of cement based materials and fiber cement slurry interface zone were closely related, and heat curing could accelerate the hydration of cement, but inevitably enlarge the defect.

A Study of Ca-Mg Silicate Crystalline Glazes——An Analysis on Forms of Crystals

In the study on Ca-Mg silicate crystalline glazes, we found some disequilibrated crystallization phenomena, such as non-crystallographic small angle forking and spheroidal growth, parasitism and wedging-form of crystals, dendritic growth, secondary nucleation, etc. Those phenomena possibly resulted from two factors: (1) partial temperature gradient, which is caused by heat asymmetry in the electrical resistance furnace, when crystals crystalize from silicate melt; (2) constitutional supercooling near the surface of crystals. The disparity of disequilibrated crystallization phenomena in different main crystalline phases causes various morphological features of the crystal aggregates. At the same time, disequilibrated crystallization causes great stress retained in the crystals, which results in cracks in glazes when the temperature drops. According to the results, the authors analyzed those phenomena and displayed correlative figures and data.

Characterization of Dissimilar Welding between 304 Stainless Steel and Gray Iron Using Nickel Coated Electrode

This paper presents the study carried out to study the microstructure and mechanical properties of AISI 304 stainless steel and gray iron, in order to recognize the effect of welding parameters on the joint. The shielded metal arc welding technique was applied with a 3.2 mm diameter nickel coated electrode under preheating and post heat conditions at 350&deg;C. Vickers hardness test and metallographic analysis were carried out at the heat affected zone and at the interface to determine the effect on mechanical and metallurgical characteristics. Vickers hardness differences among joint areas were directly related to microstructural changes. There are no significant differences in AISI 304 hardness, but the hardness increased at the heat affected zone and decreased at the filler metal. Grey iron hardness at the heat affected zone was even lower and more slightly superior than grey iron hardness.

Computational fluid dynamics simulation of friction stir welding:A comparative study on different frictional boundary conditions

Numerical simulation based on computational fluid dynamics （CFD） is a useful approach for quantitatively investigating the underlying thermal-mechanical conditions during FSW, such as temperature field and material deformation field. One of the critical issues in CFD simulation of FSW is the use of the frictional boundary condition, which represents the friction between the welding tool and the workpiece in the numerical models. In this study, three-dimensional numerical simulation is conducted to analyze the heat transfer and plastic deformation behaviors during the FSW of AA2024. For comparison purposes, both the boundary velocity （BV） models and the boundary shear stress （BSS） models are employed in order to assess their performances in predicting the temperature and material deformation in FSW. It is interesting to note that different boundary conditions yield similar predictions on temperature, but quite different predictions on material deformation. The numerical predictions are compared with the experimental results. The predicted deformation zone geometry by the BSS model is consistent with the experimental results while there is large difference between the predictions by the BV models and the experimental measurements. The fact that the BSS model yields more reasonable predictions on the deformation zone geometry is attributed to its capacity to automatically adjust the contact state at the tool/workpiece interface. Based on the favorable predictions on both the temperature field and the material deformation field, the BSS model is suggested to have a better performance in numerical simulation of FSW than the BV model.

Clustering compression-based computation-efficient calibration method for digital twin modeling of HVAC system

Digital twin is regarded as the next-generation technology for the effective operation of heating,ventilation and air conditioning(HVAC)systems.It is essential to calibrate the digital twin models to match them closely with real physical systems.Conventional real-time calibration methods cannot satisfy such requirements since the computation loads are beyond acceptable tolerances.To address this challenge,this study proposes a clustering compression-based method to enhance the computation efficiency of digital twin model calibration for HVAC systems.This method utilizes clustering algorithms to remove redundant data for achieving data compression.Moreover,a hierarchical multi-stage heuristic model calibration strategy is developed to accelerate the calibration of similar component models.Its basic idea is that once a component model is calibrated by heuristic methods,its optimal solution is utilized to narrow the ranges of parameter probability distributions of similar components.By doing so,the calibration process can be guided,so that fewer iterations would be used.The performance of the proposed method is evaluated using the operational data from an HVAC system in an industrial building.Results show that the proposed clustering compression-based method can reduce computation loads by 97%,compared to the conventional calibration method.And the proposed hierarchical heuristic model calibration strategy is capable of accelerating the calibration process after clustering and saves 14.6%of the time costs.

Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review

The built environment sector is responsible for almost one-third of the world’s final energy consumption. Hence, seeking plausible solutions to minimise building energy demands and mitigate adverse environmental impacts is necessary. Artificial intelligence (AI) techniques such as machine and deep learning have been increasingly and successfully applied to develop solutions for the built environment. This review provided a critical summary of the existing literature on the machine and deep learning methods for the built environment over the past decade, with special reference to holistic approaches. Different AI-based techniques employed to resolve interconnected problems related to heating, ventilation and air conditioning (HVAC) systems and enhance building performances were reviewed, including energy forecasting and management, indoor air quality and occupancy comfort/satisfaction prediction, occupancy detection and recognition, and fault detection and diagnosis. The present study explored existing AI-based techniques focusing on the framework, methodology, and performance. The literature highlighted that selecting the most suitable machine learning and deep learning model for solving a problem could be challenging. The recent explosive growth experienced by the research area has led to hundreds of machine learning algorithms being applied to building performance-related studies. The literature showed that existing research studies considered a wide range of scope/scales (from an HVAC component to urban areas) and time scales (minute to year). This makes it difficult to find an optimal algorithm for a specific task or case. The studies also employed a wide range of evaluation metrics, adding to the challenge. Further developments and more specific guidelines are required for the built environment field to encourage best practices in evaluating and selecting models. The literature also showed that while machine and deep learning had been successfully applied in building energy efficiency research, most of the studies are still at the experimental or testing stage, and there are limited studies which implemented machine and deep learning strategies in actual buildings and conducted the post-occupancy evaluation.

Simulating dispatchable grid services provided by flexible building loads:State of the art and needed building energy modeling improvements

End-use electrical loads in residential and commercial buildings are evolving into flexible and cost-effective resources to improve electric grid reliability,reduce costs,and support increased hosting of distributed renewable generation.This article reviews the simulation of utility services delivered by buildings for the purpose of electric grid operational modeling.We consider services delivered to(1)the high-voitage bulk power system through the coordinated action of many,distributed building loads working together,and(2)targeted support provided to the operation of low-voltage electric distribution grids.Although an exhaustive exploration is not possible,we emphasize the ancillary services and voltage management buildings can provide and summarize the gaps in our ability to simulate them with traditional building energy modeling(BEM)tools,suggesting pathways for future research and development.