Bottleneck Tree Analysis (BOTA) with green and lean index for process capacity debottlenecking in industrial refineries. (16th March 2020)

• Record Type: Journal Article
• Authors: Teng, Sin Yong; How, Bing Shen; Leong, Wei Dong; Teoh, Jun Hao; Lam, Hon Loong
• Is Part Of: Chemical engineering science. Volume 214(2020)
• Publication Date: 2020-03-16
• Subjects: Capacity debottlenecking -- Lean and green -- Multiple criteria decision making -- Expert system -- Reversed onion model -- Bottleneck Tree Analysis (BOTA); Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2019.115429
• Languages: English
• ISSNs: 0009-2509

Abstract

Graphical abstract: Highlights: Bottleneck Tree Analysis (BOTA) is proposed to identify multiple levels of process bottlenecks. Green and Lean Index (GLI) is proposed to consider sustainability of the processing plant. BOTA and GLI can aid in retrofit project scheduling and planning. The framework is shown to be able to improve the plant return on investment by 51.45%. With scheduling implementation, payback period is shortened from 85 months to 65 months. Abstract: This paper presents a novel Bottleneck Tree Analysis (BOTA) to cope with the increasing capacities within industrial refineries. BOTA is a clear and concise heuristic graphical debottlenecking method that can accurately pinpoint the process capacity bottlenecks. Coupled with BOTA, four multiple criteria decision-making methods are used for an ensemble with Spearman's correlation to assess the Green and Lean Index (GLI) of industrial processes. The decision-making tool is formulated to improve operational performance with the consideration for environmental conditions. Empirically, BOTA demonstrated a debottleneck stopping mechanism which can be theoretically explained with the reversed onion model. An additional advantage is that retrofit projects can be guided by BOTA with effective scheduling. From an industrial case study, BOTA improved normalized Global Warming Potential by 94.43 %, normalized energy consumption by 93.09 % and return on investment by 58.36 %. Project implementation by scheduling also reduced payback period from 85 to 66 months.