Multi-objective optimization of integrated biodiesel production and separation system

De, Riju; Bhartiya, Sharad; Shastri, Yogendra

doi:10.1016/j.fuel.2019.01.132

Cited by 28 publications

(7 citation statements)

References 37 publications

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“…On the other hand, recently in [36] authors show that the bipolynomial regression model when applied to FAME presents a good fit, but again, it is not mentioned the amount of oil that was converted into biodiesel, only the amount of FAME present in the obtained biodiesel. It is important to emphasize that even though the biodiesel has been studied for many years, day-to-day several research works are published where optimizations are carried out, since it is necessary to perform them according to each raw material, besides being the same oil, factors such as the type of land where the farming is made or the plant family can change the fatty acids, being this variable important at the moment to perform the transesterification reaction [18,[37][38][39].…”

Section: Other Remarksmentioning

confidence: 99%

Optimization of the real conversion efficiency of waste cooking oil to fame

et al. 2022

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This work presents a polynomial regression model for the optimization of the content of fatty acid methyl esters (FAME) and the conversion yield of waste vegetable oil to biodiesel. The equations are optimized to obtain the maximum FAME yield, which is the product of the conversion yield and the FAME content in the biodiesel. The independent variables considered are the type of catalyst used (KOH and NaOH), percentage of catalyst (0.6%, 1.0% and 1.5% w/w with respect to oil), and the methanol: oil molar ratio (6:1, 7.5:1 and 9:1). The prediction models are obtained by using nine experimental points for each catalyst. The validation is developed with four main experimental points from the mapping. A polynomial relation is obtained as a consequence, which correlates each of the experimental variables with the FAME and conversion yield. The optimization of the proposed models shows an error of 2.66% for the FAME, and an error of less than 1% for the conversion yield are obtained. This work presents a straightforward methodology to obtain the best chemical conditions in the production of biodiesel by using a small number of experiments, obtaining good results. This methodology can be applied for biodiesel production from any raw material, recalculating each of the regression constants thus allowing to obtain the highest quantity of oil to be converted in FAME.

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Section: Other Remarksmentioning

confidence: 99%

Optimization of the real conversion efficiency of waste cooking oil to fame

et al. 2022

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“…One of the most widely used MOO algorithms for the optimization of engineering problems is the non-dominated sorting hybrid algorithm [24]. In the last five years, there has been a significant usage of MOO in chemical engineering [25][26][27][28][29][30][31][32][33]. The advantage of MOO over SOO is that it allows for a multi-criteria analysis for choosing the best solution [26].…”

Section: Dynamic Process Intensification and Forced Periodic Operation Analysismentioning

confidence: 99%

Rapid Multi-Objective Optimization of Periodically Operated Processes Based on the Computer-Aided Nonlinear Frequency Response Method

et al. 2020

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The dynamic optimization of promising forced periodic processes has always been limited by time-consuming and expensive numerical calculations. The Nonlinear Frequency Response (NFR) method removes these limitations by providing excellent estimates of any process performance criteria of interest. Recently, the NFR method evolved to the computer-aided NFR method (cNFR) through a user-friendly software application for the automatic derivation of the functions necessary to estimate process improvement. By combining the cNFR method with standard multi-objective optimization (MOO) techniques, we developed a unique cNFR–MOO methodology for the optimization of periodic operations in the frequency domain. Since the objective functions are defined with entirely algebraic expressions, the dynamic optimization of forced periodic operations is extraordinarily fast. All optimization parameters, i.e., the steady-state point and the forcing parameters (frequency, amplitudes, and phase difference), are determined rapidly in one step. This gives the ability to find an optimal periodic operation around a sub-optimal steady-state point. The cNFR–MOO methodology was applied to two examples and is shown as an efficient and powerful tool for finding the best forced periodic operation. In both examples, the cNFR–MOO methodology gave conditions that could greatly enhance a process that is normally operated in a steady state.

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“…On the other hand, the optimal time not only affects the heat duty, but exceeding this time may result in a reversible reaction, decreasing the process yield [34][35][36]. Recent efforts have determined the optimal temperature profile as a key parameter for yield in biodiesel batch operations [37][38][39].…”

Section: Case Study: Production Of Biodiesel In a Batch Reactormentioning

confidence: 99%

A New Approach to Solving Stochastic Optimal Control Problems

et al. 2019

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A conventional approach to solving stochastic optimal control problems with time-dependent uncertainties involves the use of the stochastic maximum principle (SMP) technique. For large-scale problems, however, such an algorithm frequently leads to convergence complexities when solving the two-point boundary value problem resulting from the optimality conditions. An alternative approach consists of using continuous random variables to capture uncertainty through sampling-based methods embedded within an optimization strategy for the decision variables; such a technique may also fail due to the computational intensity involved in excessive model calculations for evaluating the objective function and its derivatives for each sample. This paper presents a new approach to solving stochastic optimal control problems with time-dependent uncertainties based on BONUS (Better Optimization algorithm for Nonlinear Uncertain Systems). The BONUS has been used successfully for non-linear programming problems with static uncertainties, but we show here that its scope can be extended to the case of optimal control problems with time-dependent uncertainties. A batch reactor for biodiesel production was used as a case study to illustrate the proposed approach. Results for a maximum profit problem indicate that the optimal objective function and the optimal profiles were better than those obtained by the maximum principle.

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Multi-objective optimization of integrated biodiesel production and separation system

Cited by 28 publications

References 37 publications

Optimization of the real conversion efficiency of waste cooking oil to fame

Optimization of the real conversion efficiency of waste cooking oil to fame

Rapid Multi-Objective Optimization of Periodically Operated Processes Based on the Computer-Aided Nonlinear Frequency Response Method

A New Approach to Solving Stochastic Optimal Control Problems

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