Debottlenecking cogeneration systems under process variations: Multi-dimensional bottleneck tree analysis with neural network ensemble

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU143851" target="_blank" >RIV/00216305:26210/21:PU143851 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0360544220322751" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0360544220322751</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.energy.2020.119168" target="_blank" >10.1016/j.energy.2020.119168</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Debottlenecking cogeneration systems under process variations: Multi-dimensional bottleneck tree analysis with neural network ensemble

Popis výsledku v původním jazyce

Due to lucrative economics and energy policies, cogeneration systems have blossomed in many existing industries and became their backbone technology for energy generation. With ever-increasing energy demands, the required capacity of cogeneration gradually grows yearly. This situation unveils a crawling problem in the background where many existing cogeneration systems require more energy output than their allocated design capacity. To debottleneck cogeneration systems, this work extends the bottleneck tree analysis (BOTA) towards multi-dimensional problems with novel consideration of data-driven uncertainty modelling and multi-criteria planning approaches. First, cogeneration systems were modelled using an ensemble neural network with mass and energy balance to quantify the system uncertainty while assessing energy, environment, and economic indicators in the system. These indicators are then evaluated using a multi-criteria decision making (MCDM) method to perform bottleneck tree analysis (BOTA), which identifies optimal pathways to plan for debottlenecking projects in a multi-train cogeneration plant case study. With zero initial investment and only reinvestments with profits, the method achieved 54.2 % improvement in carbon emission per unit power production, 46.3 % improvement in operating expenditure, 59.0 % improvement in heat energy production, and 58.9 % improvement in power production with a shortest average payback period of 93.9 weeks.

Název v anglickém jazyce

Debottlenecking cogeneration systems under process variations: Multi-dimensional bottleneck tree analysis with neural network ensemble

Popis výsledku anglicky

Due to lucrative economics and energy policies, cogeneration systems have blossomed in many existing industries and became their backbone technology for energy generation. With ever-increasing energy demands, the required capacity of cogeneration gradually grows yearly. This situation unveils a crawling problem in the background where many existing cogeneration systems require more energy output than their allocated design capacity. To debottleneck cogeneration systems, this work extends the bottleneck tree analysis (BOTA) towards multi-dimensional problems with novel consideration of data-driven uncertainty modelling and multi-criteria planning approaches. First, cogeneration systems were modelled using an ensemble neural network with mass and energy balance to quantify the system uncertainty while assessing energy, environment, and economic indicators in the system. These indicators are then evaluated using a multi-criteria decision making (MCDM) method to perform bottleneck tree analysis (BOTA), which identifies optimal pathways to plan for debottlenecking projects in a multi-train cogeneration plant case study. With zero initial investment and only reinvestments with profits, the method achieved 54.2 % improvement in carbon emission per unit power production, 46.3 % improvement in operating expenditure, 59.0 % improvement in heat energy production, and 58.9 % improvement in power production with a shortest average payback period of 93.9 weeks.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20303 - Thermodynamics

Projekt

<a href="/cs/project/EF16_026%2F0008413" target="_blank" >EF16_026/0008413: Strategické partnerství pro environmentální technologie a produkci energie</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Energy

ISSN

0360-5442

e-ISSN

1873-6785

Svazek periodika

215

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

19

Strana od-do

„119168-1“-„119168-19“

Kód UT WoS článku

000596834000016

EID výsledku v databázi Scopus

2-s2.0-85095748401