Development of instability analysis for the filling process of human-induced pluripotent stem cell products

Nair, A.; Horiguchi, Ikki; Fukumori, Kazuhiro; Kino‐oka, Masahiro

doi:10.1016/j.bej.2022.108506

Cited by 1 publication

(1 citation statement)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can perform unsupervised learning outlier detection on large amounts of continuous data, and while it is not applicable for high-dimensional data, it has a wide range of applications. For example, Nair et al, (2022) [17] performed outlier detection on data and assessed the stability of the filling process of human-induced multifunctional stem-cell products, Liu et al, (2022) [18] used Isolated Forest as a method for surface water quality anomaly detection, while Lin et al, (2020) [19] combined Isolated Forest with neural network models for effective wind-power prediction. Similarly, Ahmed et al, (2019) [20] employed the principle of the Isolated Forest algorithm, i.e., constructing the shortest mean path length to identify erroneous data in grid data and reduce system losses.…”

Section: Isolated Forest Model (If)mentioning

confidence: 99%

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

Wang²,

Li³

et al. 2022

Sustainability

View full text Add to dashboard Cite

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.

show abstract