Multi-objective optimal integration of biorefineries using NSGA-II and MOGWO

Punnathanam, Varun; Sivadurgaprasad, Chinta; Kotecha, Prakash

doi:10.1109/iceeot.2016.7755459

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…GA was also used by Sikalo et al (2015) for optimizing the reaction mechanisms of hydrogen, methane and tert-butanol. Punnathanam et al (2016) applied GA in the integration of a biorefinery consuming the effluent Black Liquor from a plant of paper and cellulose for producing electricity and steam. Later, Darkwah et al (2018) applied GA as a process engineering tool in the design and optimization of fermentation-based biorefineries.…”

Section: Introductionmentioning

confidence: 99%

Kinetic insights into the lignocellulosic biomass-based levulinic acid production by a mechanistic model

et al. 2020

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In this work, a mechanistic model was developed to simulate the kinetics of the production of levulinic acid (LA) from sugarcane bagasse (SCB), rice husk (RH) and soybean straw (SS). The production of LA from those agro-industrial wastes followed the methodology of biorefining in three stages. Experimental data from the third stage (catalytic depolymerization of cellulose) obtained under a wide range of operating conditions were used to estimate the parameters of the model. An optimization procedure based on a genetic algorithm was used to determine the optimal parameter values. The prediction of the concentrations of glucose, 5-HMF and LA using the mechanistic model was particularly accurate, as demonstrated by R 2 and RMSE. Thereby, a satisfactory concordance was reached between the high yields of LA of 61.1 mol%, 67.7 mol% and 61.4 mol% calculated by the model, and the experimental yields of 60.5 ± 2.1 mol%, 65.2 ± 2.9 mol% and 61.5 ± 4.0 mol% (under optimum conditions of 190°C, 7.0% w/v of H 2 SO 4 , 75 min) for SCB, RH and SS, respectively. The biorefining of the agro-industrial wastes under optimum operating conditions allowed a satisfactory catalytic depolymerization of cellulose, regardless of the degree of crystallinity. The estimation of yields led to suggesting a strategy from the point of view of process synthesis and design, integrating SCB, RH and SS to supply their off-season and, thus, to ensure the supply of raw materials in the production of LA.

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Section: Introductionmentioning

confidence: 99%

Kinetic insights into the lignocellulosic biomass-based levulinic acid production by a mechanistic model

et al. 2020

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“…Real-world project optimization problems usually consider multiple objectives to obtain the most attractive solution [39,40]. In this paper, the e-constraint method along with well-known metaheuristic methods including NSGA-II, MOPSO, and MOGWO employed to solve the problem, which are briefly described as follows.…”

Section: Solution Methodologymentioning

confidence: 99%

Application of Three Metaheuristic Algorithms to Time-Cost-Quality Trade-Off Project Scheduling Problem for Construction Projects Considering Time Value of Money

et al. 2021

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Time, cost, and quality have been known as the project iron triangles and substantial factors in construction projects. Several studies have been conducted on time-cost-quality trade-off problems so far, however, none of them has considered the time value of money. In this paper, a multi-objective mathematical programming model is developed for time-cost-quality trade-off scheduling problems in construction projects considering the time value of money, since the time value of money, which is decreased during a long period of time, is a very important matter. Three objective functions of time, cost, and quality are taken into consideration. The cost objective function includes holding cost and negative cash flows. In this model, the net present value (NPV) of negative cash flow is calculated considering the costs of non-renewable (consumable) and renewable resources in each time period of executing activities, which can be mentioned as the other contribution of this study. Then, three metaheuristic algorithms including multi-objective grey wolf optimizer (MOGWO), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective particle swarm optimization (MOPSO) are applied, and their performance is evaluated using six metrics introduced in the literature. Finally, a bridge construction project is considered as a real case study. The findings show that considering the time value of money can prevent cost overrun in projects. Additionally, the results indicate that the MOGWO algorithm outperforms the NSGA-II and MOPSO algorithms.

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“…As already mentioned before, the bivalent design goals of biorefineries (i.e., mitigating the effects of climate change whilst still being an economically viable enterprise) demand for optimising any proposed design considering both these objectives simultaneously, rendering a multi-objective optimisation problem (Tay et al, 2011;Geraili and Romagnoli, 2015;Punnathanam et al, 2016;Rodríguez Carpio et al, 2021;Riveros-Gomez et al, 2022).…”

Section: Multi-objective Optimisationmentioning

confidence: 99%

Critical analysis of the use of white-box versus black-box models for multi-objective optimisation of small-scale biorefineries

De Buck,

Sbarciog,

Cras

et al. 2023

Front. Food. Sci. Technol.

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Biorefinery systems that are embedded in their local setting provide an attractive framework for the valorisation of locally available food- and other bio-waste streams. They can aid in the provision of local bio-waste processing facilities as well as the targeted revalorisation of local bio-waste feedstocks by converting them in locally desired biorefinery products. Since food- and other bio-waste feedstocks are often diffuse feedstocks, small-scale biorefineries that are tailored for their local setting are the most suitable biorefining system for their processing. Whereas small-scale biorefineries cannot rely on the economy-of-scale to be an economic sustainable endeavour, they need to be meticulously optimised according to multiple sustainability objectives. These objectives can be of economic, societal, or environmental nature. A commonly used optimisation criterion in these problems is the energy requirements of the entire biorefinery system. For many commonly used biorefinery processes mass balance models are available (which are often mechanistic models), however, energy balances are difficult to obtain. Chemical process simulators, like Aspen Plus, provide an extensive toolkit to easily model the mass- and energy balances of a multitude of chemical processes. However, especially in the context of multi-objective optimisation, the obtained white-box models are too complex to simulate the considered processes efficiently consecutively. Therefore, in this contribution, a critical analysis is presented of the use of white-box versus the black-box models in the context of the multi-objective optimisation of a small-scale biorefinery. An in-house developed biorefinery network is re-modelled in Aspen Plus and used as a digital twin for the development of a surrogate model. Eventually, the modelled biorefinery network is optimised using both models and a comprehensive evaluation is drafted.

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Multi-objective optimal integration of biorefineries using NSGA-II and MOGWO

Cited by 7 publications

References 9 publications

Kinetic insights into the lignocellulosic biomass-based levulinic acid production by a mechanistic model

Kinetic insights into the lignocellulosic biomass-based levulinic acid production by a mechanistic model

Application of Three Metaheuristic Algorithms to Time-Cost-Quality Trade-Off Project Scheduling Problem for Construction Projects Considering Time Value of Money

Critical analysis of the use of white-box versus black-box models for multi-objective optimisation of small-scale biorefineries

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