图灵机的哲学意义

人类在创造人工自然的过程在中,如果不遵循自然规律而一意孤行,其结果必然对天然自然造成级大破坏,人类最终将自食苦果,计算机技术的发展也不例外。     

Upgrading automation for nuclear fuel in-core management: From the symbolic generation of configurations, to the neural adaptation of heuristics

FUELCON is an expert system in nuclear engineering. Its task is optimized refueling-design, which is crucial to keep down operation costs at a plant. FUELCON proposes sets of alternative configurations of fuel-allocation; the fuel is positioned in a grid representing the core of a reactor. The practitioner of in-core fuel management uses FUELCON to generate a reasonably good configuration for the situation at hand. The domain expert, on the other hand, resorts to the system to test heuristics and discover new ones, for the task described above. Expert use involves a manual phase of revising the ruleset, based on performance during previous iterations in the same session. This paper is concerned with a new phase: the design of a neural component to carry out the revision automatically. Such an automated revision considers previous performance of the system and uses it for adaptation and learning better rules. The neural component is based on a particular schema for a symbolic to recurrent-analogue bridge, called NIPPL, and on the reinforcement learning of neural networks for the adaptation.     

Maintenance scheduling to support the operation of manufacturing and production assets

Manufacturing and production plants operate physical assets that deteriorate with usage and time, thus, maintenance actions are required to restore the assets back to their original predetermined operational conditions. In this paper, we extend previous work on maintenance scheduling to considering a multi-component system that optimises both cost and reliability simultaneously. The model uses the concept of imperfect maintenance and includes factors such as ageing due to the operation rate of the system, downtime for maintenance and lead time for spare parts. For each maintenance planning period, the model predicts which of the three possible actions (namely, maintaining the component, replacing the component or doing nothing) for each component should be taken, such that the reliability is at least at a required level and the net present cost for the entire planning period is minimised. The entire approach is illustrated through the use of a numerical example and the evaluation of the model is done by using an evolutionary algorithm.     

Sustainable asset integrity management: Strategic imperatives for economic renewable energy generation

This paper develops a framework for sustainable asset integrity management (AIM) with regards to renewable energy generation plants. The authors conclude that increased downtime, low energy output, high cost of maintenance and repair operations, which are attributable to poor assets integrity management, can be mitigated with sustainable AIM. The enhancement of economic and efficient energy generation in renewable energy plants, therefore, involves a structured procedure that combines socio-economic and environmental demands in decision supports for facilities management. This can be achieved utilizing a function interfaced organizational model and techniques that include mitigation, prevention and regulatory programmes. Environmental conscious planning, review and task execution in AIM are vital to health, safety and environmental conservation whilst improved asset lifecycle performance can be reached through competence, compliance, control, communication and co-operation of management and personnel. In conclusion, proper coordination of AIM through an accurate understanding of the stakeholder demands results in efficient renewable energy generation.     

Predicting downtime costs of tracked hydraulic excavators operating in the UK opencast mining industry

This paper describes the development of a model to predict the hourly cost of downtime (using regression equations) for tracked hydraulic excavators operating in the UK opencast mining industry. A three-stage process was utilized for the model's development. The first stage predicted machine cycle times, the second predicted hire costs per hour and the third used the outputs of the first two to forecast the cost of breakdown. Both cycle time and hire cost models were revealed to be good predictors, as exhibited by the ‘high’ R² values of 0.86 and 0.95, respectively. A plant expert employed within the Defence Logistics Organisation, UK Ministry of Defence, validated these regression models and the process by which downtime costs were predicted. Future research work will aim to enhance the predictive ability of the models developed, expand the research to cover other machine types, and reproduce the findings in graphical and tabular format to improve the interpretation of information generated.     

On the effect of downtime costs and budget constraint on preventive and replacement policies

This work proposes a general approach to study and improve the effectiveness of the system with respect to its expected life-cycle cost rate. The model we propose considers a production system which is protected against demand fluctuations and failure occurrences with elements like stock piles, line and equipment redundancy, and the use of alternative production methods. These design policies allow to keep or minimize the effect on the nominal throughput, while corrective measures are taken. The system is also subject to an aging process which depends on the frequency and quality of preventive actions. Making decisions is difficult because of discontinuities in intervention and downtime costs and the limited budget. We present a non-linear mixed integer formulation that minimizes the expected overall cost rate with respect to repair, overhaul and replacement times and the overhaul improvement factor proposed in the literature. The model is deterministic and considers minimal repairs and imperfect overhauls. We illustrate its application with a case based on a known benchmark example.     

Monitoring the availability of electrostatic precipitators (ESP) in automated biomass combustion plants

To achieve emission limit values on particulate matter emissions of less than 20 mg·m⁻³ at 11% O₂ volume fraction for 1 MW–5 MW, automated biomass combustion plants are often equipped with electrostatic precipitators (ESPs). To ensure low emissions, a high availability of the precipitators, i.e. a high ratio between the uptime of the ESP and the uptime of the boiler, has to be guaranteed including operation at part load and during start-up. In the present work, an investigation on seven heating plants with tube-type and plate-type ESP in the size range of 450 kW–3.5 MW was conducted. The signals on load, temperatures, fans, lambda sensors, voltage and currency were measured during two years. From these data, the availability is determined based on specific definitions for the operation modes of the ESP and the boiler. To evaluate threshold values for the ESP operation and to validate the method, gravimetric measurements on the particulate matter emissions in different operation modes were performed.The investigation reveals that ESPs in today's biomass plants can achieve availabilities of greater than 90%. Further, malfunctions and maintenance issues that are not immediately repaired are identified as main source for low availabilities, while availabilities of greater than 95% are considered achievable for new plants. To ensure optimum ESP operation with low particle emissions under real-life conditions, a reliable monitoring of the availability based on the presented method is recommended for automated biomass combustion plants.     

A model for predicting plant maintenance costs

A model is presented that predicts the total cost of plant maintenance (i.e. direct cost of maintenance plus indirect cost of lost production) and is derived studying a random sample of tracked hydraulic excavators. Analysis is based on the machine history file data of 33 plant items, modelled using multiple regression (MR) analysis. Validation of the model was determined via the combination of an observed high R 2 at 0.94 and various statistical tests which confirmed the prerequisites of a rigorous MR analysis. Machine weight, type of industry and company attitude towards predictive maintenance were found to be the best predictor variables of total plant maintenance cost. The paper also discusses reasons underlying the inclusion of predictor variables in the final model, and concludes with clear directions for future research in this field.     

Efficiency and effectiveness of wind farms—keys to cost optimized operation and maintenance

Operation and maintenance (O&M) cost will be the key to the economic viability of large offshore wind farms planned worldwide. In order to support investment decisions a systematic mathematical approach to the O&M cost contributions is required prior to detailed engineering or even construction of the wind farm.Adopting the general terms of efficiency, productivity and effectiveness defined for production processes we introduce the Wind Farm Process and its Total Overall Equipment Effectiveness (TotalOEE) by considering wind farms performing a transformation of produced electrical energy to delivered (sold) electrical energy. This transformation process consists of an installation, i.e. properly selected wind energy converters and their arrangement to form a wind farm, and of a process comprising operation and maintenance. Both are the subject of optimization to maximize the annual energy output by minimizing the different kinds of losses.In a systematic approach to the causes and nature of losses in wind farms the terms theoretical production time, available production time and valuable production time are redefined in unit full load hours. Then, a calculation scheme is developed to quantify wind farm production losses in terms of planned or unplanned downtimes and speed losses and to relate the associated reduction of revenues ΔR to the theoretical maximum of annual wind park revenues R_theo(park). It leads to the simple equation ΔR/R_theo = TotalOEE – 1 < 0.     

地面保温管道的安全停输时间计算

根据管道热平衡原则和圆柱体温度分布公式,用准稳态方法探讨了地面保温管道的安全停输时间的计算问题。计算过程考虑了大气温度变化和起始停输时间点对安全停输时间的影响。计算结果表明：安全停输时间不仅与油品温度、大气温度有关,而且还与起始停输时间点有关;随着停输时间的延长,起始停输时间点对安全停输时间的影响逐渐减小;当油品温度提高到一定程度后,试图再以继续提高油品温度来延长安全停输时间效果不大,而且也不经济。