Energy and CO2 management for chemical and related industries: issues, opportunities and challenges

Vooradi, Ramsagar; Anne, Sarath Babu; Tula, Anjan K.; Eden, Mario R.; Gani, Rafiqul

doi:10.1186/s42480-019-0008-6

Cited by 23 publications

(17 citation statements)

References 54 publications

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“…However, there is a clear lack of research in industrial symbiosis networks that integrate renewable energy, which was observed by Butturi et al (2019). In addition, there is a need to combine emission reduction technologies with improving energy and resource use efficiency in chemical industries (Vooradi et al, 2019). Therefore, this work aims to address these gaps, utilizing the model developed by Ahmed et al (2020) with the goal of designing a carbon neutral industrial park to produce value-added products from free or waste resources (seawater, air, sunlight, and carbon dioxide).…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Sustainable Carbon Negative Eco-Industrial Parks

2021

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Growing climate change concerns in recent years have led to an increased need for carbon dioxide emission reduction. This can be achieved by implementing the concept of circular economy, which promotes the practice of resource conservation, emission minimization, and the maintenance of sustainable revenue streams. A considerable amount of carbon dioxide emissions is a consequence of stationary sources from industrial processes. These emissions can be reduced using carbon capture utilization and storage (CCUS) or reduced at source by using emission free renewable resources. The method developed within this work uses mixed integer linear programming (MILP) to design sustainable clusters that convert seawater (including waste brine), air, and waste carbon dioxide emissions to value-added products with sunlight as the main energy source. In this way, circular economy is employed to minimize fresh resource consumption and maximize material reuse. The potential of this work is demonstrated through a case study, which shows that an industrial park may be profitable while adhering to strict emission and material constraints.

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Section: Introductionmentioning

confidence: 99%

Synthesis of Sustainable Carbon Negative Eco-Industrial Parks

2021

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“…The chemical processing industry manufacturing products by mixing, separating, forming, and chemical reactions, is being promoted for its greatly important contribution to economic development, providing high quality products to many sectors, such as building, transportation, construction and health [1]. It is also the largest energy consumer among industrial sectors with 1078 million tons of oil equivalent of energy consumption in the year 2016 [2]. Unlike general artificial intelligence which builds computerized systems possessing intelligence like humans, industrial intelligence is more concerned with the application of technologies to build a smart system that can adapt to the changes in industrial environment.…”

Section: Introductionmentioning

confidence: 99%

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations

Fang

Zhou

Wang

et al. 2021

Front. Chem. Sci. Eng.

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Modeling and optimization is crucial to smart chemical process operations. However, a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations, chemical reactions and separations. This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity. Thus, this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties. An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method. Firstly, a data set was generated based on process mechanistic simulation validated by industrial data, which provides sufficient and reasonable samples for model training and testing. Secondly, four well-known machine learning methods, namely, K-nearest neighbors, decision tree, support vector machine, and artificial neural network, were compared and used to obtain the prediction models of the processes operation. All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features. Finally, optimal process operations were obtained by using the particle swarm optimization approach.

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“…One of the most important goals of today's society is the transition from a fossil fuelbased economy to a sustainable bioeconomy with the aim of mitigating global greenhouse gas (GHG) emissions, the alarming increase in global energy demand, and the exhaustion of fossil fuel reserves [1]. In this context, biomass is a renewable source with constant global availability that can be used for the manufacture of a wide variety of bio-products, including chemicals, materials, food, and feed [2].…”

Section: Introductionmentioning

confidence: 99%

Influence of Cellulose Characteristics on Pyrolysis Suitability

et al. 2021

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Cellulose is the most abundant component of biomass and the one that requires the most activation energy (Ea) for pyrolysis. In this study, the dependence of Ea on the intrinsic cellulose characteristics, such as the degree of polymerization (DP), crystallinity, and crystal size, was studied in different cellulose samples, including samples from Eucalyptus globulus, Ulmus minor, Linun usitatissimum, Olea europaea, Robinia pseudoacacia, and Populus alba. Then, to describe the pyrolytic degradation of cellulose, the Ozawa–Flynn–Wall kinetic method was the most appropriate among the isoconversional models studied. An acceptable quadratic relationship of R2 > 0.9 between the Ea values of the different cellulose samples with their corresponding DP, crystallinity index, and crystal size values was found. Therefore, low crystallinity and low-to-medium crystal size values are desired to obtain lower Ea values for cellulose pyrolysis. On the other hand, DP did not present a clear effect on Ea in the studied DP range.

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Energy and CO2 management for chemical and related industries: issues, opportunities and challenges

Cited by 23 publications

References 54 publications

Synthesis of Sustainable Carbon Negative Eco-Industrial Parks

Synthesis of Sustainable Carbon Negative Eco-Industrial Parks

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations

Influence of Cellulose Characteristics on Pyrolysis Suitability

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