Modelling SER Biomass Gasification Using Dynamic Neural Networks

Salah, Alia; Hanel, Lutz; Beirow, Marcel; Scheffknecht, Günter

doi:10.1016/b978-0-444-63428-3.50008-4

Cited by 12 publications

(8 citation statements)

References 5 publications

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“…Under high temperatures, waste materials are rapidly broken into molecules and atoms. The main components released during plasma gasification are CO, H2, and CH4 [52].…”

Section: Plasma Gasificationmentioning

confidence: 99%

Perspective of Bioenergy in Pakistan for Sustainable Eco-Friendly Energy Substitute: A Review

Maitlo

Abbasi

Korai

et al. 2021

Mehran Univ. res. j. eng. technol.

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Pakistan has remained an energy deficient country for the last few decades. The supply of electricity decreased in 2002-2007 against the exponentially increasing demand in Pakistan. In the meantime, severe load shedding was started across Pakistan that negatively affected the economic growth of the country. More than 63% of the population in rural areas live without electricity. The government cut down electricity supply eight to twelve hours per day in terms of load shedding that adversely affects the living standards and economic growth of the nation. In the world, Pakistan stands on 133 regarding ranking in energy consumption. Energy usage per capita in Pakistan is 482 kilograms of oil equivalent per year (kgoe/a) which is very low when compared with the industrialized countries. Besides that Pakistan strongly adheres with non-renewable energy options to mollify its energy demand. The import of fossil fuels to meet energy demand adversely affects the economy of Pakistan. This review helps in the identification of indigenous available biomass resources and their conversion processes for power production to improve power generation, living standards and economy of the country. The application of indigenous biomass resources for electricity generation will reduce the pressure caused by increased demand for energy.

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“…Under high temperatures, waste materials are rapidly broken into molecules and atoms. The main components released during plasma gasification are CO, H2, and CH4 [52].…”

Section: Plasma Gasificationmentioning

confidence: 99%

Perspective of Bioenergy in Pakistan for Sustainable Eco-Friendly Energy Substitute: A Review

Maitlo

Abbasi

Korai

et al. 2021

Mehran Univ. res. j. eng. technol.

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“…In an open loop NARX model training, a feedforward multilayer neural network is trained using backpropagation algorithms to define main structure of neural network. Afterwards, in a closed loop, NARX model training model outputs are estimated on current and previous inputs together with previously estimated outputs (making a closed loop) [27]. A detailed explanation of NARX structure can be found in Chen and Billings [28].…”

Section: Development and Training Of Narx Network Consist Of 2 Steps Namely An Open Loop Narx Model Training And Closed Loop Narx Model Tmentioning

confidence: 99%

“…The maximum prediction error of gas outlet temperature was 7.4%. For modelling of biomass gasification in fluidised bed reactors, NARX models were used to predict syngas temperature, flow rate and pressure in a 200 kWth sorption enhanced reforming steam gasification plant [27]. NARX models seem to be a promising approach to describe non-linear systems with significant delays where accumulation of mass and energy is considered.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a NARX model will be developed to predict syngas temperature and content of hydrogen (H2), carbon monoxide (CO) and methane (CH4) in a 75 kWth fixed bed gasifier, operated by TU Dresden. A similar approach has been used in Salah et al [27] but for fluidised bed gasifiers which usually operate at higher pressures and where process changes are much faster. Compared to the dynamic neural network model and the research done in Mikulandric et al [24] the model should be able to predict syngas temperature based on raw measured data of fuel and air flow rates and without any prior knowledge of process dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Modelling of Temperature and Syngas Composition in a Fixed Bed Biomass Gasifier using Nonlinear Autoregressive Networks

Mikulandrić¹,

Böhning²,

Lončar³

2020

J. sustain. dev. energy water environ. syst.

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To improve biomass gasification efficiency through process control, a lot of attention had been given to development of models that can predict process parameters in real time and changing operating conditions. The paper analyses the potential of a nonlinear autoregressive exogenous model to predict syngas temperature and composition during plant operation with variable operating conditions. The model has been designed and trained based on measurement data containing fuel and air flow rates, from a 75 kWth fixed bed gasification plant at Technical University Dresden. Process performance changes were observed between two sets of measurements conducted in 2006 and 2013. The effect of process performance changes on the syngas temperature was predicted with prediction error under 10% without changing the model structure. It was concluded that the model could be used for short term predictions (up to 5 minutes) of syngas temperature and composition as it strongly depends on current process measurements for future predictions. For long term predictions other types of dynamic neural networks are more applicable.

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“…However, it is approximated in the training phase of the feedforward neural network model. The neural network NARX model can be implemented using an open loop or a closed loop architecture [8]. The open loop architecture, favors online process monitoring whereas with the closed loop architecture, future process outputs can be estimated offline, for example through process simulation.…”

Section: A Neural Network-nonlinear Autoregressive Exogenousmentioning

confidence: 99%

Process Monitoring Platform based on Industry 4.0 tools: a waste-to-energy plant case study

Kabugo

Jämsä‐Jounela

Schiemann³

et al. 2019

2019 4th Conference on Control and Fault Tolerant Systems (SysTol)

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This work presents a process data analytics platform built around the concept of industry 4.0. The platform utilizes the state-of-the-art industry internet of things (IIoT) platforms, machine learning (ML) algorithms and big-data software tools. The industrial applicability of the platform was demonstrated by the development of soft sensors for use in a waste-to-energy (WTE) plant. In the case study, the work studied data-driven soft sensors to predict syngas heating value and hot flue gas temperature. From data-driven models, the neural network based nonlinear autoregressive with external input (NARX) model demonstrated better performance in prediction of both syngas heating value and flue gas temperature in a WTE process.

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Modelling SER Biomass Gasification Using Dynamic Neural Networks

Cited by 12 publications

References 5 publications

Perspective of Bioenergy in Pakistan for Sustainable Eco-Friendly Energy Substitute: A Review

Perspective of Bioenergy in Pakistan for Sustainable Eco-Friendly Energy Substitute: A Review

Modelling of Temperature and Syngas Composition in a Fixed Bed Biomass Gasifier using Nonlinear Autoregressive Networks

Process Monitoring Platform based on Industry 4.0 tools: a waste-to-energy plant case study

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