A nonparametric analysis of energy environmental Kuznets Curve in Chinese Provinces

Energy resources are an important material foundation for the survival and development of human society, and the relationship between energy and economy is interactive and complementary. This paper analyzes the energy consumption–economic growth nexus in Chinese provinces using novel and recent nonparametric time-series as well as panel data empirical approaches. The dataset covers 30 provinces over the period of 1980–2018. The empirical analysis indicates the presence of a nonlinear functional form and smooth structural changes in most of the provinces. The nonparametric empirical analysis validates the presence of a nonlinear unit root problem in energy consumption and economic growth, and nonlinear cointegration between the variables. Additionally, the nonparametric panel cointegration test reports evidence of convergence in energy consumption and economic growth patterns across the provinces. The nonparametric regression analysis finds economic growth to have a positive effect, on average, on energy consumption in all provinces, except for Beijing. Further, the energy environmental Kuznets curve exists between economic growth and energy consumption in 20 out of 30 Chinese provinces. The Granger causality analysis reveals the presence of a mixed causal relationship between economic growth and energy consumption. The empirical findings have important implications for Chinese authorities in planning for improving energy efficiency, decoupling between economic growth and energy consumption, and reducing the environmental footprint of provinces.     

New Paradigm of Data-Driven Smart Customisation through Digital Twin

Big data is one of the most important resources for the promotion of smart customisation. With access to data from multiple sources, manufacturers can provide on-demand and customised products. However, existing research of smart customisation has focused on data generated from the physical world, not virtual models. As physical data is constrained by what has already occurred, it is limited in the identification of new areas to improve customer satisfaction. A new technology called digital twin aims to achieve this integration of physical and virtual entities. Incorporation of digital twin into the paradigm of existing data-driven smart customisation will make the process more responsive, adaptable and predictive. This paper presents a new framework of data-driven smart customisation augmented by digital twin. The new framework aims to facilitate improved collaboration of all stakeholders in the customisation process. A case study of the elevator industry illustrates the efficacy of the proposed framework.     

Sustainability-guided life-cycle design and assessment for bio-based composite foams: Integrate flame retardancy/lightweight in usage and energy utilization after service

Sustainable development strategy has aroused a great interest in biomass resources as alternative raw materials. A kind of biomass-derived poly(butylene succinate) (PBS), has been developed as porous foams to reduce resource exhaustion and meet lightweight demands. For fire-safety in-service, graphene oxide (GO) was functionalized by 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) to combine flame-retardant elements and heat-barrier function. Hence, a very low loading level of P-containing GO as only 5 wt% could reduce peak heat release rate (pHRR) and total heat release (THR) of PBS-based foams by 58.5% and 22.3%, respectively. Meanwhile, N-/P-doped mesoporous char with a specific surface area of 136 m²/g, which derived from combustion of flame-retardant foaming PBS, contributes to a potential of energy storage applications in the capacitor or the anode of Li-ion battery with long-term stability. Overall, the sustainability of bio-based polyester could integrate lightweight of foaming, and be extended to utilization after use via facile combustion inspired by flame-retardancy design.     

激光扫描速度对碳钢板表面锈蚀层去除质量的影响

采用光纤激光器开展了碳钢板表面锈蚀层激光清洗研究,通过白光干涉仪、光学显微镜、拉曼光谱仪等研究了激光扫描速度对锈蚀层去除质量的影响。研究表明,当激光扫描速度小于2 000 mm/s时,因光斑搭接率高,热累积效应强,试样表面出现基材熔化重凝现象,同时试样表面发生二次氧化,生成了复杂的铁的氧化物膜层,此时试样表面粗糙度最小。当激光扫描速度增加到3000 mm/s时,试样表面锈蚀层去除干净,露出金属基底本身色泽,基材表面二次氧化减弱。当线速度继续增加时,因光斑搭接率低,锈蚀层吸收的激光能量少,仅有部分锈蚀被去除,试样表面开始出现残留锈蚀层,且随着线速度的增加,残留锈蚀层和试样表面粗糙度增加。通过调节扫描速度可以获得较好的除锈效果,工艺优化后,激光功率为120 W时,除锈效率达到1.5 m2/h。     

Inspection strategy for availability improvement

In this paper, an inspection strategy is proposed for systems subject to random shocks and whose state is only known through inspection. The shocks magnitudes and times between shocks arrivals are random variables. Each shock induces an amount of damage to the system which is gradually weakened as damage accumulates. The system fails when the total damage exceeds some threshold level. The proposed inspection strategy suggests to inspect the system at predetermined times T₁, T₂,...T _k . If failure is detected, then the system is repaired to a state as good as new, otherwise, it is kept operating. The expression of the system availability is derived. A numerical procedure is developed to generate an inspection sequence which maximizes the system availability. The proposed strategy has been developed for some critical subsystems of trees harvesters. An illustrative example is provided.     

Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization

A novel model predictive fault-tolerant control (MPFTC) strategy adopting genetic algorithm (GA) is proposed for batch processes under the case of disturbances and partial actuator faults. Based on the extended state space model in which the tracking error is contained, there are more degrees of freedom provided for the controller design and better control performance is obtained. In order to enhance the control performance further, the GA is introduced to optimize the relevant weighting matrices in the cost function. The effectiveness of the proposed MPFTC approach is tested on the injection velocity regulation of the injection molding process.     

考虑目标机动和落角约束的二阶滑模制导律

针对末制导阶段导弹以期望的终端落角攻击高机动目标的需求,将Super-twisting算法与自适应滑模扰动观测器相结合,提出一种满足终端落角约束的二阶滑模制导律。目标机动带来的干扰导致系统扰动的上界未知,将目标加速度视为系统扰动,设计自适应滑模扰动观测器对系统扰动进行在线估计,通过对观测器增益进行自适应调整,克服传统观测器选取增益时依赖扰动上界的缺陷。设计改进的Super-twisting算法作为制导律的趋近律,在降低抖振的同时使制导律可充分利用导弹的过载能力,从而提升系统的收敛速度,解决传统Super-twisting算法中系统状态远离平衡点时收敛速度慢的问题。基于李雅普诺夫稳定性理论,证明该制导系统能在有限时间内收敛。数学仿真结果表明：自适应滑模扰动观测器和所设计制导律有效,自适应滑模扰动观测器能够准确跟踪系统扰动,所设计的制导律能够满足期望的终端落角约束,且具有较高的命中精度,脱靶量小于0.2 m。