The Experts below are selected from a list of 26844 Experts worldwide ranked by ideXlab platform
Zhenlei Wang - One of the best experts on this subject based on the ideXlab platform.
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multiobjective optimization of ethylene cracking furnace system using self adaptive multiobjective teaching learning based optimization
Energy, 2018Co-Authors: Lyndon While, Liang Zhao, Mark Reynolds, Xin Wang, J J Liang, Zhenlei WangAbstract:The ethylene cracking furnace system is crucial for an olefin plant. Multiple cracking Furnaces are used to convert various hydrocarbon feedstocks to smaller hydrocarbon molecules, and the operational conditions of these Furnaces significantly influence product yields and fuel consumption. This paper develops a multiobjective operational model for an industrial cracking furnace system that describes the operation of each furnace based on current feedstock allocations, and uses this model to optimize two important and conflicting objectives: maximization of key products yield, and minimization of the fuel consumed per unit ethylene. The model incorporates constraints related to material balance and the outlet temperature of transfer line exchanger. The self-adaptive multiobjective teaching-learning-based optimization algorithm is improved and used to solve the designed multiobjective optimization problem, obtaining a Pareto front with a diverse range of solutions. A real industrial case is investigated to illustrate the performance of the proposed model: the set of solutions returned offers a diverse range of options for possible implementation, including several solutions with both significant improvement in product yields and lower fuel consumption, compared with typical operational conditions.
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Cyclic scheduling for an ethylene cracking furnace system using diversity learning teaching-learning-based optimization
Computers & Chemical Engineering, 2017Co-Authors: Lyndon While, Mark Reynolds, Xin Wang, Zhenlei WangAbstract:Abstract The ethylene cracking furnace system is central to an olefin plant. Multiple cracking Furnaces are employed for processing different hydrocarbon feeds to produce various smaller hydrocarbon molecules, such as ethylene, propylene, and butadiene. We develop a new cyclic scheduling model for a cracking furnace system, with consideration of different feeds, multiple cracking Furnaces, differing product prices, decoking costs, and other more practical constraints. To obtain an efficient scheduling strategy and the optimal operational conditions for the best economic performance of the cracking furnace system, a diversity learning teaching-learning-based optimization (DLTLBO) algorithm is used to simultaneously determine the optimal assignment of multiple feeds to different Furnaces, the batch processing time and sequence, and the optimal operational conditions for each batch. The performance of the proposed scheduling model and the DLTLBO algorithm is illustrated through a case study from a real-world ethylene plant: experiments show that the new algorithm out-performs both previous studies of this set-up, and the basic TLBO algorithm.
R G C Beerkens - One of the best experts on this subject based on the ideXlab platform.
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energy saving options for glass Furnaces recovery of heat from their flue gases and experiences with batch cullet pre heaters applied in the glass industry
69th Conference on Glass Problems 4 November 2008 through 5 November 2008 Columbus OH 1 30 143-162, 2009Co-Authors: R G C BeerkensAbstract:Several measures, such as changes in batch composition (less batch humidity), or optimization of operating conditions, and limiting the combustion air excess, can lead to typically 2-8 % of energy savings of industrial glass Furnaces. Larger energy savings are only possible by new furnace designs, more insulation, increased cullet ratios or extra recovery of the heat contents of the flue gases. The flue gas heat contents, downstream regenerators or recuperators of air-fired Furnaces or downstream the exhaust of oxygen-fired glass melting Furnaces, can be exploited to preheat batch and cullet up to 275-350 °C for regenerative Furnaces and up to more than 500 °C for recuperative and oxygen-fired Furnaces. Above 550-600 °C, the batches may start to show sticking behaviour. Energy savings of 12-20 % have been reported for regenerative air-fired glass Furnaces after connecting a batch and pellet-preheat system. The highest savings in the consumption of specific energy (energy consumption per metric ton molten glass), can be achieved by combining the application of batch and cullet preheating with an increased pull rate. Increased pull rates of more than 10 % have been achieved and this potential of increasing production in the same furnace will improve economics of batch / cullet pre-heaters. Almost all batch or cullet pre-heater applications are found in the container glass industry. The pay-back of the capital costs of batch & cullet preheating systems by energy cost savings may take more than 3-4 years, depending on the energy prices and modifications required to the batch and flue gas channel systems. There are 5 or 6 different types of batch and/or cullet preheating systems applied in the glass industry or still in a testing phase. All pre-heaters deliver very dry batch or cullet. This dry batch charged into the furnace may cause batch carry-over (depending on the doghouse design and position of the burners relative to the batch blanket moving from the doghouse into the tank). Especially in end-port fired Furnaces, this dust formation in the furnace may lead to fouling of the regenerator checkers. Other problems that have been reported are: odour in case of high levels of externally recycled cullet (with organic contamination) and dust formation and dust deposition in the ambient space surrounding the furnace, especially in the case of long distances between pre-heater and furnace. Direct contact pre-heaters (direct contact between the flue gases and the batch, to be preheated) will show acid gas scrubbing potential. Batch components absorb SOx, HCI, HF and selenium compounds from the flue gas stream.
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energy saving options for glass Furnaces recovery of heat from their flue gases and experiences with batch cullet pre heaters applied in the glass industry
69th Conference on Glass Problems 4 November 2008 through 5 November 2008 Columbus OH 1 30 143-162, 2009Co-Authors: R G C BeerkensAbstract:Several measures, such as changes in batch composition (less batch humidity), or optimization of operating conditions, and limiting the combustion air excess, can lead to typically 2-8 % of energy savings of industrial glass Furnaces. Larger energy savings are only possible by new furnace designs, more insulation, increased cullet ratios or extra recovery of the heat contents of the flue gases. The flue gas heat contents, downstream regenerators or recuperators of air-fired Furnaces or downstream the exhaust of oxygen-fired glass melting Furnaces, can be exploited to preheat batch and cullet up to 275-350 °C for regenerative Furnaces and up to more than 500 °C for recuperative and oxygen-fired Furnaces. Above 550-600 °C, the batches may start to show sticking behaviour. Energy savings of 12-20 % have been reported for regenerative air-fired glass Furnaces after connecting a batch and pellet-preheat system. The highest savings in the consumption of specific energy (energy consumption per metric ton molten glass), can be achieved by combining the application of batch and cullet preheating with an increased pull rate. Increased pull rates of more than 10 % have been achieved and this potential of increasing production in the same furnace will improve economics of batch / cullet pre-heaters. Almost all batch or cullet pre-heater applications are found in the container glass industry. The pay-back of the capital costs of batch & cullet preheating systems by energy cost savings may take more than 3-4 years, depending on the energy prices and modifications required to the batch and flue gas channel systems. There are 5 or 6 different types of batch and/or cullet preheating systems applied in the glass industry or still in a testing phase. All pre-heaters deliver very dry batch or cullet. This dry batch charged into the furnace may cause batch carry-over (depending on the doghouse design and position of the burners relative to the batch blanket moving from the doghouse into the tank). Especially in end-port fired Furnaces, this dust formation in the furnace may lead to fouling of the regenerator checkers. Other problems that have been reported are: odour in case of high levels of externally recycled cullet (with organic contamination) and dust formation and dust deposition in the ambient space surrounding the furnace, especially in the case of long distances between pre-heater and furnace. Direct contact pre-heaters (direct contact between the flue gases and the batch, to be preheated) will show acid gas scrubbing potential. Batch components absorb SOx, HCI, HF and selenium compounds from the flue gas stream.
Chaowei Liu - One of the best experts on this subject based on the ideXlab platform.
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Cyclic Scheduling for Ethylene Cracking Furnace System with Consideration of Secondary Ethane Cracking
Industrial & Engineering Chemistry Research, 2010Co-Authors: Chuanyu Zhao, Chaowei LiuAbstract:Cracking Furnaces of ethylene plants are capable of processing multiple feeds to produce smaller hydrocarbon molecules, such as ethylene, propylene, and ethane. The best practice for handling the produced ethane is to recycle it as an internal feed and conduct the secondary cracking in a specific furnace. As cracking Furnaces have to be periodically shut down for decoking, when multiple Furnaces processing different feeds under various product values and manufacturing costs are considered, the operational scheduling for the entire furnace system should be optimized to achieve the best economic performance. In this paper, a new MINLP (mixed-integer nonlinear programming) model has been developed to optimize the operation of cracking furnace systems with the consideration of secondary ethane cracking. This model is more practical than the previous study and can simultaneously identify the allocation of feeds with their quantity, time, and sequence information for each cracking furnace. A case study has demo...
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cyclic scheduling for best profitability of industrial cracking furnace system
Computers & Chemical Engineering, 2010Co-Authors: Chaowei Liu, Jia ZhangAbstract:An ethylene plant employs multiple cracking Furnaces in parallel to convert various hydrocarbon feedstocks to smaller hydrocarbon molecules, mostly ethylene and propylene. The continuous operational performance of cracking Furnaces gradually decays because of coke formation in the reaction coils, which requires each furnace to be periodically shut down for decoking. Given multiple feeds and different cracking Furnaces as well as various product prices and manufacturing costs, the operational scheduling for the entire furnace system should be optimized to achieve the best economic performance. In this paper, a new MINLP (mixed-integer nonlinear programming) model has been developed to obtain cyclic scheduling strategies for cracking furnace systems. Compared to previous studies, the new model has more capabilities to address operation profitability of multiple feeds cracked in multiple Furnaces. Meanwhile, it inherently avoids unpractical conditions such as simultaneous shutdown of multiple Furnaces. Case studies demonstrate the efficacy of the developed methodology.
Lyndon While - One of the best experts on this subject based on the ideXlab platform.
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multiobjective optimization of ethylene cracking furnace system using self adaptive multiobjective teaching learning based optimization
Energy, 2018Co-Authors: Lyndon While, Liang Zhao, Mark Reynolds, Xin Wang, J J Liang, Zhenlei WangAbstract:The ethylene cracking furnace system is crucial for an olefin plant. Multiple cracking Furnaces are used to convert various hydrocarbon feedstocks to smaller hydrocarbon molecules, and the operational conditions of these Furnaces significantly influence product yields and fuel consumption. This paper develops a multiobjective operational model for an industrial cracking furnace system that describes the operation of each furnace based on current feedstock allocations, and uses this model to optimize two important and conflicting objectives: maximization of key products yield, and minimization of the fuel consumed per unit ethylene. The model incorporates constraints related to material balance and the outlet temperature of transfer line exchanger. The self-adaptive multiobjective teaching-learning-based optimization algorithm is improved and used to solve the designed multiobjective optimization problem, obtaining a Pareto front with a diverse range of solutions. A real industrial case is investigated to illustrate the performance of the proposed model: the set of solutions returned offers a diverse range of options for possible implementation, including several solutions with both significant improvement in product yields and lower fuel consumption, compared with typical operational conditions.
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Cyclic scheduling for an ethylene cracking furnace system using diversity learning teaching-learning-based optimization
Computers & Chemical Engineering, 2017Co-Authors: Lyndon While, Mark Reynolds, Xin Wang, Zhenlei WangAbstract:Abstract The ethylene cracking furnace system is central to an olefin plant. Multiple cracking Furnaces are employed for processing different hydrocarbon feeds to produce various smaller hydrocarbon molecules, such as ethylene, propylene, and butadiene. We develop a new cyclic scheduling model for a cracking furnace system, with consideration of different feeds, multiple cracking Furnaces, differing product prices, decoking costs, and other more practical constraints. To obtain an efficient scheduling strategy and the optimal operational conditions for the best economic performance of the cracking furnace system, a diversity learning teaching-learning-based optimization (DLTLBO) algorithm is used to simultaneously determine the optimal assignment of multiple feeds to different Furnaces, the batch processing time and sequence, and the optimal operational conditions for each batch. The performance of the proposed scheduling model and the DLTLBO algorithm is illustrated through a case study from a real-world ethylene plant: experiments show that the new algorithm out-performs both previous studies of this set-up, and the basic TLBO algorithm.
Kengo Saito - One of the best experts on this subject based on the ideXlab platform.
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Indium atomic absorption signals from non-pyrolytic and pyrolytically coated graphite Furnaces in electrothermal atomization atomic absorption spectrometry
Journal of Analytical Atomic Spectrometry, 1996Co-Authors: Shoji Imai, Noriyuki Hasegawa, Yasuhisa Hayashi, Kengo SaitoAbstract:In the ETAAS determination of In, double and single peak signals were observed when non-pyrolytic and pyrolytic graphite (PG) Furnaces, respectively, were used. Since Arrhenius plots for the single peak signal in the PG furnace were composed of two straight line segments, it was proposed that the single peak signal possibly comprises two unresolved pulses. The effects of Zr- and W-treated Furnaces, and O2 and CO additives, on the charring temperature, atomic absorption response, pulse shape, appearance temperature of atomic absorption response and kinetic data were investigated. The following processes were proposed as atomization mechanisms in both Furnaces: for the first pulse, direct heterogeneous reduction of In2O(g) on the hot graphite wall to form In(g); for the second pulse, direct reduction of In2O3 on the hot graphite wall to form the metal followed by atomization via gaseous In dimers. It was also found that the sensitivity loss reaction, proposed as the thermal dissociation of In2O3(s) to form In2O(g), is catalyzed by carbon atoms from the furnace wall.
