Upgrading Pathways of Intelligent Manufacturing in China: Transitioning across Technological Paradigms

Intelligent technologies are leading to the next wave of industrial revolution in manufacturing. In developed economies, firms are embracing these advanced technologies following a sequential upgrading strategy—from digital manufacturing to smart manufacturing (digital-networked), and then to new-generation intelligent manufacturing paradigms. However, Chinese firms face a different scenario. On the one hand, they have diverse technological bases that vary from low-end electrified machinery to leading-edge digital-network technologies; thus, they may not follow an identical upgrading pathway. On the other hand, Chinese firms aim to rapidly catch up and transition from technology followers to probable frontrunners; thus, the turbulences in the transitioning phase may trigger a precious opportunity for leapfrogging, if Chinese manufacturers can swiftly acquire domain expertise through the adoption of intelligent manufacturing technologies. This study addresses the following question by conducting multiple case studies: Can Chinese firms upgrade intelligent manufacturing through different pathways than the sequential one followed in developed economies? The data sources include semi-structured interviews and archival data. This study finds that Chinese manufacturing firms have a variety of pathways to transition across the three technological paradigms of intelligent manufacturing in non-consecutive ways. This finding implies that Chinese firms may strategize their own upgrading pathways toward intelligent manufacturing according to their capabilities and industrial specifics; furthermore, this finding can be extended to other catching-up economies. This paper provides a strategic roadmap as an explanatory guide to manufacturing firms, policymakers, and investors.This research is supported by the National Natural Science Foundation of China (91646102, L1824039, L1724034, L1624045, and L1524015), the project of China’s Ministry of Education “Humanities and Social Sciences (Engineering and Technology Talent Cultivation)” (16JDGC011), CAE Advisory Project “Research on the strategy of Manufacturing Power towards 2035” (2019-ZD-9), the National Science and Technology Major Project “High-end Numerical Control and Fundamental Manufacturing Equipment” (2016ZX04005002), Beijing Natural Science Foundation Project (9182013), the Chinese Academy of Engineering’s China Knowledge Center for Engineering Sciences an Technology Project (CKCEST-2019-2-13, CKCEST-2018-1-13, CKCEST-2017-1-10, and CKCEST-2015-4-2), the UK–China Industry Academia Partnership Programme (UK-CIAPP\260), as well as the Volvo-supported Green Economy and Sustainable Development Tsinghua University (20153000181) and Tsinghua Initiative Research Project (2016THZW)

Simulation and Optimization of Wind Farm Operations under Stochastic Conditions

This dissertation develops a new methodology and associated solution tools to achieve optimal operations and maintenance strategies for wind turbines, helping reduce operational costs and enhance the marketability of wind generation. The integrated framework proposed includes two optimization models for enabling decision support capability, and one discrete event-based simulation model that characterizes the dynamic operations of wind power systems. The problems in the optimization models are formulated as a partially observed Markov decision process to determine an optimal action based on a wind turbine's health status and the stochastic weather conditions. The rst optimization model uses homogeneous parameters with an assumption of stationary weather characteristics over the decision horizon. We derive a set of closed-form expressions for the optimal policy and explore the policy's monotonicity. The second model allows time-varying weather conditions and other practical aspects. Consequently, the resulting strategy are season-dependent. The model is solved using a backward dynamic programming method. The bene ts of the optimal policy are highlighted via a case study that is based upon eld data from the literature and industry. We nd that the optimal policy provides options for cost-e ective actions, because it can be adapted to a variety of operating conditions. Our discrete event-based simulation model incorporates critical components, such as a wind turbine degradation model, power generation model, wind speed model, and maintenance model. We provide practical insights gained by examining di erent maintenance strategies. To the best of our knowledge, our simulation model is the rst discrete-event simulation model for wind farm operations. Last, we present the integration framework, which incorporates the optimization results in the simulation model. Preliminary results reveal that the integrated model has the potential to provide practical guidelines that can reduce the operation costs as well as enhance the marketability of wind energy

Using precision livestock farming (PLF) technologies to assess the impact of environmental stressors on animal welfare and production efficiency on modern dairy farms

In modern dairy farming systems, heat stress is still a significant challenge. Dairy cows will encounter sub-optimal welfare which can result in production decline, diseases and even mortality, especially for high-producing cows with lower heat tolerance. The frequency and magnitude of heat stress events or heat waves are predicted to keep increasing in coming decades associated with global warming. Therefore, greater attention is being paid to alleviating the effects of heat stress on dairy cows and livestock generally. Modelling and on-farm experiments have been undertaken in many countries to assess the influence of heat stress on livestock using modern computer technologies and other hi-tech tools. At the same time, mitigation approaches such as optimal shed structure, new cooling facilities, targeted feeding regimes, improved farm management and genetic selection have all been studied extensively. However, due to differences between farm conditions and varying heat tolerance of different breeds and coping ability, the results from different heat stress models provided a variety of thresholds for on-farm decision support. Therefore, determination of accurate heat stress thresholds to facilitate practical mitigation options are still difficult. This study was initiated by summarizing the progresses achieved by previous studies on intensively kept dairy cows in relation to measuring, assessing and mitigating their heat stress. By taking comparative analysis of the published studies about thermal indices, animal responses and mitigation solutions, a range of recommendations were given for developing more accurate assessment and designing of more effective mitigation options. The review suggested that for achieving accurate and applicable thresholds of heat stress, it is necessary to establish monitoring systems embedded into routine farm management systems, which can be an add-on unit of current robotic milking system (RMS). The robust monitoring system would measure real-time data from the ambient environment, animal responses, as well as the operation pattern of mitigations. Furthermore, by facilitating big-data analysis techniques to be used on individual farms, (or for individual animal) it might be possible to implement self-calibration procedure for the assessment, thresholds and control algorithms responding to varied cow’s production status, farm management factors and local climate changes. The follow-up research presented in this thesis demonstrated the possibility of establishing more accurate heat stress threshold by taking advantage of the routinely collected datasets on robotic dairy farms and local weather stations. The dairy farm observed in this study situated in a subtropical climate region, held around 150 lactating cows and applied RMS with semi-free traffic. The farm management system recorded specific production, health and behaviour information of each individual animal over 5-year period (2013-2017), which was utilized for the analysis in this study. The historical climate conditions were measured by local weather station with dataset accessible on a government website, which provided the data of daily thermal parameters for this research. Furthermore, data-loggers were also positioned on farm from April 2016 to November 2017 to measure thermal parameters hourly. By using the collected information, this study compared the performance of published thermal comfort indices (TCIs) as the indicators of cows’ responses to heat stress. These TCIs included temperature humidity index (THI), black globe humidity index (BGHI), environmental stress index (ESI), equivalent temperature index (ETI), heat load index (HLI), respiration rate index (RR) and comprehensive climate index (CCI). The comparison also included the basic thermal parameters: dry bulb temperature (Tdb), relative humidity (RH), wet bulb temperature (Twb) and dew point temperature (Tdp). The strength of their correlation with daily milk yield (DMY) and milk temperature (MT) was tested statistically. The regression analysis using climate dataset from local weather station and on-farm data-loggers were also compared to validate the accuracy of online data source. The statistical analysis found similar performance between TCIs and Tdb. It was also found that the inaccuracy of online data source, due to spatial variability between on-farm measurement and local weather station, could be neglected when modelling the association between TCIs and MT. A general threshold with significant decline of DMY was identified as THI>64 for cows with DMY around 31 kg/cow/day. As Tdb can provide sufficient accuracy in the prediction of heat stress, the dynamic thresholds of daily minimum and mean temperature (Tmin and Tmean) were then established using individual information of 126 cows. The dataset was grouped according to the age, body weight (BW) and days in milk (DIM) of cows. Specific thresholds for different groups were identified using single broke-line regression between temperature and DMY or MT. Machine learning model was applied to transform these thresholds of different group into a decision tree of dynamic thresholds, which achieved overall 94% accuracy with the thresholds of Tmin, and 79% accuracy with the thresholds of Tmean. Moreover, for the whole herd, multiple broken-line regression was applied, which established four stages of heat stress including as thermal comfort stage (Tmin 14 oC, Tmean > 16 oC) based on the change of DMY and MT. To gain more understanding of the heat stress influence on animal behaviours in RMS, extra dependent variables were imported into new models involving rumination time (RT), time of milking (TM), miking frequency (MF), milking duration (MD), milking speed (MS), and milk yield per milking (MY). A new index – rumination efficiency index (REI) was created to evaluate the efficiency of rumination. According to the multiple broken-line regression, 5 minutes reduction of RT, 0.08 kg/cow/hour reduction of REI and 1% increase of low efficiency miking (LEM) were found to be associated with raising 1 oC of Tmean. It was also demonstrated that cows could not adjust their pattern of milking behaviour (e.g. visiting time pattern) coping with heat stress. Statistically, 86% of their milking event happened between 07:00 AM and 09:00 AM. However, REI and RMS performance can be improved by adjusting the pattern of milking behaviour such as milking interval (MI). The financial comparison between current pattern and adjusted pattern estimated that nearly $400 daily benefit could be gained. In addition, this study also analysed the cumulative and lag effect of heat stress which were time-related. For the short-term effect, an intensity duration index (IDI) was defined by multiplying the mean temperature of the heat stress period with the duration of the period. Multiple levels of heat stress were then identified by IDI with different decline rate of DMY from -0.01 to -0.13 kg/cow/IDI. For long-term heat stress, the lag and cumulative effect was demonstrated by the negative correlation between the duration of heat stress during dry-off period and the production performance of the subsequent lactation period. The lag effect was found to be 3-4 days, while the cumulative effect could last for about 2 months. The regression between DMY and the average temperature of the period with heat stress during the 2 months before test day (HSmean) was found to perform stronger correlation (R2 equals 0.73-0.77) than the regression between DMY and same day’s temperature (R2 equals 0.65-0.68)

Holistic framework for land settlement development project sustainability assessment : comparison of El Hierro Island hydro wind project and Sivens dam project

Project developer in the domain of land settlement project are involved with many stakeholders and are usually overflown by data relative to technical, economic and social issues. This paper contributes to the necessary multi-scale approach challenge and we propose a holistic framework that enables to describe the development process of land settlement project and assess its sustainability. It would help developers to take decisions compliant with the project complexity. In the model driven engineering perspective, the metamodel framework is described with the ISO 19440 four views to represent complex systems: architectural, structural, functional and behavioural. We confront it to describe two case studies: the successful project of hydro-wind power plant in El Hierro in the Canaries, and the Sivens Dam project in France sadly famous for its deadly outcome. Their comparison enables us to draw hypothesis on what are the ingredients of success and validate the framework

Business Intelligence in the Vineyard

The evolution that is nowadays taking place in the information and communication fields, namely in mobile computing and remote monitoring, constitutes a very interesting challenge to the agricultural sector. This reality places agronomic knowledge in centre stage as these technologies are dramatically improving data collection and storage capacities, challenging the farmers and the agricultural field experts to develop processes that efficiently transform data into information and knowledge and are able to support the everyday decision making at farm level. In this work we will present a demonstration project under way in a vineyard in Portugal where we are exploring the potential of the most recent technological innovations available in the market to build the i-Farm, the information and knowledge society intelligent farm. i-Farm (intelligent farm) applies at farm level the potential offered by using in an integrated way mobile solutions, sensor networks, wireless communication and digital imagery materialized in a information system that supports farmer real time decision making in the field and in the office. The i-Farm project creates a unique knowledge repository containing information from multiple sources (crop, environment, soil, operations, market, etc.) enabling accurate and timely decisions. For the project development a Business Intelligence approach is used. In the context of this paper this broad term is used to refer to the process of aggregating, processing and building rich and relevant information which is made available dynamically in real time to managers in an interactive way to support decisions and planning activitiesinfo:eu-repo/semantics/publishedVersio

Heat stress risk in European dairy cattle husbandry under different climate change scenarios - uncertainties and potential impacts

[EN] In the last decades, a global warming trend was observed. Along with the temperature increase, modifications in the humidity and wind regime amplify the regional and local impacts on livestock husbandry. Direct impacts include the occurrence of climatic stress conditions. In Europe, cows are economically highly relevant and are mainly kept in naturally ventilated buildings that are most susceptible to climate change. The high-yielding cows are particularly vulnerable to heat stress. Modifications in housing management are the main measures taken to improve the ability of livestock to cope with these conditions. Measures are typically taken in direct reaction to uncomfortable conditions instead of in anticipation of a long-term risk for climatic stress. Measures that balance welfare, environmental and economic issues are barely investigated in the context of climate change and are thus almost not available for commercial farms. Quantitative analysis of the climate change impacts on animal welfare and linked economic and environmental factors is rare. Therefore, we used a numerical modeling approach to estimate the future heat stress risk in such dairy cattle husbandry systems. The indoor climate was monitored inside three reference barns in central Europe and the Mediterranean regions. An artificial neuronal network (ANN) was trained to relate the outdoor weather conditions provided by official meteorological weather stations to the measured indoor microclimate. Subsequently, this ANN model was driven by an ensemble of regional climate model projections with three different greenhouse gas concentration scenarios. For the evaluation of the heat stress risk, we considered the number and duration of heat stress events. Based on the changes in the heat stress events, various economic and environmental impacts were estimated. The impacts of the projected increase in heat stress risk varied among the barns due to different locations and designs as well as the anticipated climate change (considering different climate models and future greenhouse gas concentrations). There was an overall increasing trend in number and duration of heat stress events. At the end of the century, the number of annual stress events can be expected to increase by up to 2000, while the average duration of the events increases by up to 22 h compared to the end of the last century. This implies strong impacts on economics, environment and animal welfare and an urgent need for mid-term adaptation strategies. We anticipated that up to one-tenth of all hours of a year, correspondingly one-third of all days, will be classified as critical heat stress conditions. Due to heat stress, milk yield may decrease by about 2.8 % relative to the present European milk yield, and farmers may expect financial losses in the summer season of about 5.4 % of their monthly income. In addition, an increasing demand for emission reduction measures must be expected, as an emission increase of about 16 Gg of ammonia and 0.1 Gg of methane per year can be expected under the anticipated heat stress conditions. The cattle respiration rate increases by up to 60 %, and the standing time may be prolonged by 1 h. This causes health issues and increases the probability of medical treatments. The various impacts imply feedback loops in the climate system which are presently underexplored. Hence, future in-depth studies on the different impacts and adaptation options at different stress levels are highly recommended.This research has been supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE) (grant nos. 2814ERA02C and 2814ERA03C), the Instituto Nacional de Investigacion Tecnologia Agraria y Alimentaria (INIA) (grant no. 618105), the Basque Government (grant no. BERC 2018-2021), the Spanish Ministry of Economy, Industry and Competitiveness MINECO (grant nos. MDM-2017-0714, FJCI-2016-30263, and RYC-2017-22143), and the Innovation Foundation Denmark (grant no. 4215-00004B).Hempel, S.; Menz, C.; Pinto, S.; Galán, E.; Janke, D.; Estellés, F.; Müschner-Siemens, T.... (2019). Heat stress risk in European dairy cattle husbandry under different climate change scenarios - uncertainties and potential impacts. 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