Minimization of energy consumption in multiple stage evaporator using Genetic Algorithm

Verma, Om Prakash; Manik, Gaurav; Kant, Surya; Jain, Vinay; Jain, Deepak Kumar; Wang, Haoxiang

doi:10.1016/j.suscom.2017.11.005

Cited by 19 publications

(18 citation statements)

References 48 publications

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“…Additionally, the liquor flow rates and temperatures of vapor generated at various MSE effects along with the thermophysical variables ( h L , λ , and H ) are also presented in Table 2. The obtained result reveals that WCA yields the optimum energy performance as compared to real time plant estimation and in earlier work 13 with improvement of 16.16% and 47.67%, respectively.…”

Section: Resultsmentioning

confidence: 67%

“…In the literature, various energy reduction schemes and configurations along with the nonlinear steady-state, dynamic modeling and simulation of MSE have been reported. [3][4][5][6][7][8][9] Classical numerical techniques (iterative methods), Interior-Point Methodology (I-PM, a dynamic programming approach 10 ), Genetic Algorithm (GA, a soft computing approach, [11][12][13] ) and Water Cycle Algorithm (WCA, a metaheuristic technique) 14 have been employed to solve the steadystate nonlinear models of MSE in search of the optimum steady-state unknown process parameters. 9 In addition, a number of metaheuristic approaches (such as Squirrel Search Algorithm, 15 Political optimizer, 16 and Water strider algorithm 17 ) have been proposed for solving realtime complex optimization tasks frequently encountered in engineering applications.…”

Section: Introductionmentioning

confidence: 99%

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Water cycle algorithm tuned robust fractional‐order Proportional–Integral–Derivative controller for energy optimization and control of nonlinear Multiple Stage Evaporator: A case study of paper mill

Verma

Yadav

Pati

et al. 2021

Asia-Pacific J Chem Eng

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In paper industries, Multiple Stage Evaporator (MSE) is used to concentrate black liquor. Due to the energy intensive nature of MSE, the global energy scenario stresses on optimizing its energy efficiency. Moreover, the necessity of a tight control of product quality demands superior understanding of system dynamics for controller design. This paper presents a robust fractional‐order Proportional–Integral–Derivative (FOPID) controller for concentration control of black liquor in Heptad's effect MSE. First, the steady‐state unknown process parameters are estimated by solving the nonlinear steady‐state model. Thereafter, these optimal process parameters are utilized to simulate the nonlinear dynamic model to obtain the transfer functions. By using these transfer functions, the FOPID controller is designed whose parameters are tuned via Water Cycle Algorithm (WCA). The competence of WCA toward controller tuning is validated by comparing with other optimization techniques (Genetic Algorithm [GA], Simulated Annealing [SA], Particle Swarm Optimization [PSO], and Krill Herd [KH]). Moreover, the performance of the proposed FOPID controller in concentration control, set‐point tracking, and noise suppression is validated by comparing with conventional PID, two‐degree of freedom‐PID (2‐DOF‐PID), and Internal Model Control (IMC) controllers. The results demonstrate that FOPID controller reduces the Integral Square Error (ISE) by 91.05%, 89.81%, and 78.84%, respectively, with respect to PID, 2‐DOF‐PID, and IMC. Also, the proposed FOPID controller improves the system transience with high robustness.

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Section: Resultsmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Water cycle algorithm tuned robust fractional‐order Proportional–Integral–Derivative controller for energy optimization and control of nonlinear Multiple Stage Evaporator: A case study of paper mill

Verma

Yadav

Pati

et al. 2021

Asia-Pacific J Chem Eng

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“…Furthermore, when compared to the real-time plant estimate of SC of 2.67 kg/s ( Verma et al., 2018d ), it can be observed that Model-VIII proposed in this work ( i.e. , hybrid model of all ESSs) offers SC of 1.919 kg/s, thereby significantly reducing the SC by 28.13%.…”

Section: Resultsmentioning

confidence: 88%

“…The real-time plant data (SE and SC) have been taken from the specified real-time plant (at Saharanpur, India) and reported works ( Verma et al., 2018d , 2019 ). For enhanced assessment of the SE and SC obtained by WCA (for various operating schemes of MSE) with real-time plant data and previous research chronicles, a graphical illustration (as shown in Figure 4 ) has also been presented.…”

Section: Resultsmentioning

confidence: 99%

Energy optimization of Multiple Stage Evaporator system using Water Cycle Algorithm

Yadav

Verma

2020

Heliyon

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Black liquor, a residual stream from the Kraft recovery process of paper mills is an incipient biomass energy resource which finds prospective biofuel-based industrial applications to ensure process self-sufficiency and sustainability. Black liquor is concentrated using Multiple Stage Evaporator, the utmost energy intensive unit, before using it as biofuel. Pertaining to the contemporary global energy scenario, improvement in energy efficiency of Multiple Stage Evaporator becomes indispensable. The present work investigates the non-linear modeling and simulation-based optimization of Heptads' stage based Multiple Stage Evaporator in backward feed flow configuration integrated with various energy saving strategies. A novel metaheuristic approach, Water Cycle Algorithm has been employed to search the optimum estimates of unknown process variables and therefore, the optimum energy efficiency parameters. The optimization results demonstrate the efficiency of Water Cycle Algorithm in screening the most appropriate operating strategy, i.e. , hybrid model of all energy saving strategies (steam-split, feed-split and feed-preheating) with optimum energy efficiency i.e. Steam Economy of 7.092 and Steam Consumption of 1.919 kg/s. Moreover, a comparative analysis of the results with previous literature and real-time plant estimates reveal that the hybrid model offers improvement of 52.84% in Steam Economy and reduction in Steam Consumption by 28.13% when compared to the real plant data.

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“…GA is considered as one of the promising algorithms for solving micro-grid problems and it has proven its suitability for application in energy contexts [31]. Moreover, the GA can be used to solve a broad range of problems, such as smart grid applications [32], sizing of a multi-source PV/Wind with Hybrid Energy Storage System (HESS) [33], energy management [34], and operating costs of electricity [35]. The algorithm is widely accepted in energy systems optimization and more specifically in multi-objective methods, where a set of optimal solutions, called Pareto front is obtained [36].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

et al. 2019

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This paper proposes an optimal design for hybrid grid-connected Photovoltaic (PV) Battery Energy Storage Systems (BESSs). A smart grid consisting of PV generation units, stationary Energy Storage Systems (ESSs), and domestic loads develops a multi-objective optimization algorithm. The optimization aims at minimizing the Total Cost of Ownership (TCO) and the Voltage Deviation (VD) while considering the direct and indirect costs for the prosumer, and the system stability with regard to intermittent PV generation. The optimal solution for the optimization of the PV-battery system sizing with regard to economic viability and the stability of operation is found while using the Genetic Algorithm (GA) with the Pareto front. In addition, a fuzzy logic-based controller is developed to schedule the charging and discharging of batteries while considering the technical and economic aspects, such as battery State of Charge (SoC), voltage profile, and on/off-peak times to shave the consumption peaks. Thus, a hybrid approach that combines a Fuzzy Logic Controller (FLC) and the GA is developed for the optimal sizing of the combined Renewable Energy Sources (RESs) and ESSs, resulting in reductions of approximately 4% and 17% for the TCO and the VD, respectively. Furthermore, a sensitivity cost-effectiveness analysis of the complete system is conducted to highlight and assess the profitability and the high dependency of the optimal system configuration on battery prices.

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Minimization of energy consumption in multiple stage evaporator using Genetic Algorithm

Cited by 19 publications

References 48 publications

Water cycle algorithm tuned robust fractional‐order Proportional–Integral–Derivative controller for energy optimization and control of nonlinear Multiple Stage Evaporator: A case study of paper mill

Water cycle algorithm tuned robust fractional‐order Proportional–Integral–Derivative controller for energy optimization and control of nonlinear Multiple Stage Evaporator: A case study of paper mill

Energy optimization of Multiple Stage Evaporator system using Water Cycle Algorithm

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

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