A Strategy for Determining the Decommissioning Life of Energy Equipment Based on Economic Factors and Operational Stability

Abstract

LCC and EL models have been widely used in recent years to determine the decommissioning life of equipment in energy companies, with LCC (life-cycle cost) being the total “lifetime” cost of the equipment from the time it is put into operation until the end of its decommissioning and disposal; the average annual cost of the equipment can be calculated based on the LCC. The overall LCC can be calculated as the average annual LCC, while the EL is the age of the equipment at which its average annual LCC is the lowest. It is believed that the decommissioning of the equipment in the EL year will result in the lowest annual average equipment turnover, thus maximizing the economic benefits of the equipment. Recently, LCC and EL research has been gradually introduced to the energy field, but there remains a lack of research depth. In current practice, energy equipment LCCs are mainly determined by selecting a portion of inventoried equipment to serve as a sample record for all costs incurred. The intent is to derive the economic life of the equipment-year by directly seeking its average annual cost, but this method tends to downplay maintenance, overhaul, and other cost events as “random small probability events”. This method is also incomplete for evaluating the decommissioning life of equipment whose average annual cost strictly decreases year-by-year. In this study, we analyzed the use of 75,220 KV transformers that were put into service by an energy company in 1986 as a case study (costs for this type of equipment were first recorded strictly in terms of LCC in 1986), used Isolated Forest (IF) to screen the outliers of various types of data costs, and then probability-corrected the corrected dataset with a Welbull distribution (Welbull). Then, we employed a stochastic simulation (MC) to calculate the LCC of the equipment and determined its economic lifetime (EL) and compared the results of the stochastic simulation method with those of the traditional method to provide a more reasonable explanation for the “small probability” of cost occurrences. Next, we predicted the average cost of the equipment given a use-period of 38-41-years using AHA, Bi-LSTM, and other comparative algorithms, compared the MAE, MAPE, and RMES indexes, selected the most suitable prediction model, and produced a predicted cost under the chosen method to obtain the economic life of the equipment. Finally, we compared our results with the design life of the equipment (design life being the technical life expectancy of a product based on the expectations of the manufacturer), and determined its best retirement age by comprehensively studying and judging the economic and technical benefits. The retirement age analysis was guided by by a comprehensive study of economic and technical benefits. We refer to our decommissioning life determination model as Monte Carlo -artificial hummingbird algorithm–BiLSTM–lifecycle cost model (MC-AHABi-LCC). We found that the decommissioning life obtained by MC-AHABi-LCC is closer to the actual equipment decommissioning life than that given by standard LCC and EL analysis and that our model is more accurate and scientific.

Fusion Algorithm of the Improved A* Algorithm and Segmented Bézier Curves for the Path Planning of Mobile Robots

“…Considering the issue of path planning for mobile robots, future research directions will focus on the following four aspects. (1) The strategies to eliminate the "traps" mentioned in Section 6 will be investigated; (2) The fusion algorithm will be applied to the Turtlebot3 robot for realistic path planning simulation experiments, and the characteristics and limitations of the robot will be fully considered; (3) The fusion algorithm will be compared with other recently proposed algorithms to solve the path planning issue of mobile robots, and further verify its effectiveness; and (4) The performance of the fusion algorithm in more complex, U-shaped obstacles as well as dynamic obstacles will be further explored to verify its robustness.…”

“…The commonly used methods for calculating H(n) are Manhattan distance, Euclidean distance, and Chebyshev distance. In order to be more consistent with the actual distance when the robot moves, the Euclidean distance was used in this paper, and the calculation formula is shown in Equation (2).…”

“…In recent years, mobile robots, such as handling robots, sorting robots, and medical robots, have been widely used in industrial manufacturing, logistics, and medical fields. With the increasing popularity of mobile robots, path planning, that is, finding an optimal path from the starting point to the target point without collision for a specific obstacle environment, has become a key aspect affecting their functions and performance [1,2]. According to the mastery of the obstacle environment, path planning algorithms are usually divided into two categories, global algorithms and local algorithms.…”

“…(1) A 5-neighborhood search strategy is proposed to optimize the search path, shorten the algorithm execution time and reduce the redundant nodes. (2) The dynamic weighting factor is added to the heuristic function of the traditional A* algorithm, so that the algorithm can automatically adjust the search focus during the search process, solving the common problem of long algorithm running time of the traditional A* algorithm. (3) A key node extraction strategy is proposed to solve the problem of many redundant nodes in the original global path.…”

An Efficient Siamese Network and Transfer Learning-Based Predictive Maintenance System for More Sustainable Manufacturing

“…The design of modern production lines calls for the implementation of these modern and new pieces of machinery to achieve reliability and production-efficiency goals, to the detriment of the reuse of legacy machinery in new production lines. This is in opposition to the principles of the circular economy (CE) which calls for the reuse of such machinery in an effort to extend the useful lifespan of machines and reduce waste [1][2][3]. The extended usage of legacy machinery in modern production facilities is dependent on the ability of that machinery to be modernized through the retrofitting of sensor and communications technology to allow integration of that machine with advanced manufacturing execution systems (MES).…”