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The transformation of production mode leads to the need to strictly ensure the order in-time delivery. The global control ability of manufacturing workshops has become a necessary capability for enterprises. The real-time accurate Order Remaining Completion Time (ORCT) prediction allows managers to master the production schedule fluctuation, which provides criteria for workshop management. Due to the widespread deployment of Internet of Things (IoT) devices in the workshop, the difficulty of systematic processing and analysis of multi-dimensional heterogeneous data has been the main pain point of ORCT prediction. To tackle these problems, a Manufacturing Big Data (MBD)-driven ORCT prediction method based on Salp Swarm Algorithm (SSA) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. An attribute selection algorithm combined with max-relevance and min-redundancy and regularisation is used to mine key data from MBD. An SSA-BiLSTM model is proposed to achieve efficient and accurate ORCT prediction. The prediction model is supported by BiLSTM, and the hyperparameters are optimised by SSA. Finally, a case study about ORCT prediction in the IoT-enabled manufacturing workshop is presented. The result verifies that the proposed ORCT prediction method has obvious advantages over the other three traditional methods in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

CO2 monitoring is important for carbon emission evaluation. Low-cost and medium-precision sensors (LCSs) have become an exploratory direction for CO2 observation under complex emission conditions in cities. Here, we used a calibration method that improved the accuracy of SenseAir K30 CO2 sensors from ±30 ppm to 0.7–4.0 ppm for a CO2-monitoring instrument named the SENSE-IAP, which has been used in several cities, such as in Beijing, Jinan, Fuzhou, Hangzhou, and Wuhan, in China since 2017. We conducted monthly to yearly synchronous observations using the SENSE-IAP along with reference instruments (Picarro) and standard gas to evaluate the performance of the LCSs for indoor use with relatively stable environments. The results show that the precision and accuracy of the SENSE-IAP compared to the standard gases were rather good in relatively stable indoor environments, with the short-term (daily scale) biases ranging from −0.9 to 0.2 ppm, the root mean square errors (RMSE) ranging from 0.7 to 1.6 ppm, the long-term (monthly scale) bias ranging from −1.6 to 0.5 ppm, and the RMSE ranging from 1.3 to 3.2 ppm. The accuracy of the synchronous observations with Picarro was in the same magnitude, with an RMSE of 2.0–3.0 ppm. According to our evaluation, standard instruments or reliable standard gases can be used as a reference to improve the accuracy of the SENSE-IAP. If calibrated daily using standard gases, the bias of the SENSE-IAP can be maintained within 1.0 ppm. If the standard gases are hard to access frequently, we recommend a calibration frequency of at least three months to maintain an accuracy within 3 ppm. [ABSTRACT FROM AUTHOR]

Background: The CO2 released by humans and livestock through digestion and decomposition is an important part of the urban carbon cycle, but is rarely considered in studies of city carbon budgets since its annual magnitude is usually much lower than that of fossil fuel emissions within the boundaries of cities. However, human and livestock respiration may be substantial compared to fossil fuel emissions in areas with high population density such as Manhattan or Beijing. High-resolution datasets of CO2 released from respiration also have rarely been reported on a global scale or in cities globally. Here, we estimate the CO2 released by human and livestock respiration at global and city scales and then compare it with the carbon emissions inventory from fossil fuels in 14 cities worldwide. Results: The results show that the total magnitude of human and livestock respiration emissions is 38.2% of the fossil fuel emissions in Sao Paulo, highest amongst the 14 cities considered here. The proportion is larger than 10% in cities of Delhi, Cape Town and Tokyo. In other cities, it is relatively small with a proportion around 5%. In addition, almost 90% of respiratory carbon comes from urban areas in most of the cities, while up to one-third comes from suburban areas in Beijing on account of the siginificant livestock production. Conclution: The results suggest that the respiration of human and livestock represents a significant CO2 source in some cities and is nonnegligible for city carbon budget analysis and carbon monitoring. [ABSTRACT FROM AUTHOR]

Atmospheric inversion of carbon dioxide (CO2) measurements to understand carbon sources and sinks has made great progress over the last two decades. However, most of the studies, including four-dimension variational (4D-Var), Ensemble Kalman filter (EnKF), and Bayesian synthesis approaches, obtains directly only fluxes while CO2 concentration is derived with the forward model as post-analysis. Kang et al. (2012) used the Local Ensemble Transform Kalman Filter (LETKF) that updates the CO2, surface carbon fluxes (SCF), and meteorology field simultaneously. Following this track, a system with a short assimilation window and a long observation window was developed (Liu et al., 2019). However, this system faces the challenge of maintaining global carbon mass. To overcome this shortcoming, here we introduce a Constrained Ensemble Kalman Filter (CEnKF) approach to ensure the conservation of global CO2 mass. After a standard LETKF procedure, an additional assimilation process is applied to adjust CO2 at each model grid point and to ensure the consistency between the analysis and the first guess of global CO2 mass. In the context of observing system simulation experiments (OSSEs), we show that the CEnKF can significantly reduce the annual global SCF bias from ~0.2 gigaton to less than 0.06 gigaton by comparing between experiments with and without it. Moreover, the annual bias over most continental regions is also reduced. At the seasonal scale, the improved system reduced the flux root-mean-square error from priori to analysis by 48-90 %, depending on the continental region. Moreover, the 2015-2016 El Nino impact is well captured with anomalies mainly in the tropics. [ABSTRACT FROM AUTHOR]

Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, owing to the CO2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, Dynamic Global Vegetation Model (DGVM) simulations, and machine learning techniques, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982-2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r > 0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r = 0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extra-tropical southern hemisphere (Trop+SH) and extra-tropical northern hemisphere (NH), we found that, during 1982-2015, simulated GPP from most of the models showed a stronger increasing trend over Trop+SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual GPP was dominated by the CO2 fertilization effect (Global: 83.9 %), albeit a large uncertainty in magnitude among individual simulations. However, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO2 fertilization effect. Trends after 2000 were different from the full time-series, showing that satellite-based GPP products suggested weakened rising trends over NH and even significantly decreasing trends over Trop+SH, while the trends from DGVMs kept increasing. The inconsistencies are very likely caused by the contrasting performances between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks. [ABSTRACT FROM AUTHOR]

To prevent the spread of the COVID-19 epidemic, restrictions such as lockdown, were conducted globally, which led to significant reduction in fossil fuel emissions, especially in urban regions. However, CO2 concentrations in urban regions are affected by many factors, such as weather and background CO2 fluctuations. Thus, it is difficult to directly observe the reductions in CO2 concentrations with sparse ground observations. Here, we focus on urban ground transportation emissions, which were dramatically affected by the prohibitions, to determine the reduction signals. We conducted six on-road CO2 observations in Beijing using mobile platforms before (BC), during (DC) and after COVID-19 prohibitions (AC). To reduce the weather and background impacts, we chose trips with the most similar weather as possible and calculated the enhancement, which mean the difference in the CO2 concentration between on-road and the background level measured at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP) tower. The results showed that DC CO2 enhancement decreased by 41 parts per million (ppm) and 26 ppm compared to those during BC and AC, respectively, after eliminating the fluctuations in CO2 concentrations on polluted days. Detailed analysis showed that, during COVID, there was no difference between weekdays and weekends. The enhancements during rush hours were almost twice those during working hours, indicating that emissions during rush hours were much higher. Compared with DC and BC, the reductions in the enhancements during rush hours were much larger than those during working hours. Our findings showed a clear decrease during COVID, which are consistent with the CO2 concentration and emissions reductions due to the pandemic. The enhancement way used in this study is an effective method to reduce the impacts of weather and background fluctuation and should be regularly and more frequently conducted in future work. [ABSTRACT FROM AUTHOR]

The concept of an intelligent manufacturing system is proposed and described based on a comparison of manufacturing and biological organism systems. To promptly eliminate uncertain disturbances produced during the intelligent manufacturing system's operation, an immune monitoring model for an intelligent manufacturing system is proposed that is analogous to a biological immune system mechanism. The implementation of immune recognition, assessment, learning, memory, and control regulation in response to abnormal disturbance factors is investigated. The system's immune evaluation model based on the gray correlation analysis method is carefully analyzed. A neural network-based immune response method is used to deal with the disturbance. Finally, the paper shows how the effectiveness of the proposed method was verified experimentally through a simulation. [ABSTRACT FROM AUTHOR]