Minimization of energy consumption in multiple stage evaporator using Genetic Algorithm

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Minimization of energy consumption in multiple stage evaporator using Genetic Algorithm Om Prakash Verma a , Gaurav Manik b,∗ , Suryakant b , Vinay Kumar Jain c , Deepak Kumar Jain d , Haoxiang Wang e,f a

School of Electronics, KIIT University Bhubaneswar, Odisha, India Department of Polymer and Process Engineering, Indian Institute of Technology Roorkee, Uttarakhand, India c Department of Computer Science and Engineering, JUET, Raghogarh, M.P., India d Institute of Automation, Chinese Academy of Sciences, Beijing, China e Department of ECE, Cornell University, NY, USA f R&D Center, GoPerception Laboratory, NY, USA b

a r t i c l e

i n f o

Article history: Received 27 June 2017 Received in revised form 25 September 2017 Accepted 21 November 2017 Available online xxx Keywords: Energy efficiency Genetic Algorithm Multiple stage evaporator Steam economy Steam consumption

a b s t r a c t Maximization of the steam efficiency of a multiple stage evaporator employed for concentrating black liquor in pulp and paper mills carries immense significance and relevance in today’s scenario. Nonlinear mathematical models of heptads’ effects backward feed flow with various energy saving schemes namely, steam-split, feed-split, feed-preheating and their hybrid operations have been developed. The steam economy as a cost function translates the problem into a nonlinear optimal search problem. The mass and heat balance equations act as nonlinear equality constraints while vapor temperatures and liquor flows appear as inequality constraints. The formulated problem has been solved efficiently to attain optimal solution using Genetic Algorithm approach which demonstrates advantages of convergence and relative less sensitivity towards initial values versus conventional algorithms. The simulations indicate that a hybrid of steam-split, feed-split and feed-preheating process arrangements with backward feed flow could provide the highest heat transfer across evaporator effects with an optimum steam economy of 6.47 and consumption of 6541.93 kg/h. © 2017 Elsevier Inc. All rights reserved.

1. Introduction Among the major priorities of the present century, primarily for the developing countries such as India and China, is the replacement of fossil fuels with biomass containing waste materials for energy intensive processes. This shall alleviate the global issues arising due to an increased demand of former. The issues and challenges include higher energy costs and emission of enormous amounts of global warming causing greenhouse gases in the environment. Terms such as renewable-bio-energy, renewable-bio-products, and renewable-bio-economy are being used frequently and emphasized by proposing pertinent needs to policy makers and industrial investors. The international community, including that from India, is strongly committed towards

∗ Corresponding author. E-mail addresses: [email protected] (O.P. Verma), [email protected] (G. Manik), [email protected] ( Suryakant), [email protected] (V.K. Jain), [email protected] (D.K. Jain), [email protected] (H. Wang).

a sustainable development. Intergovernmental Panel on Climate Change (IPCC) reported an observation and quoted that “Warming of the climate system is unequivocal, and since the 1950s, many of the observed changes are unprecedented over decades to millennia. The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, and sea level has risen” [1]. However, since the last forecast cycle, there have been significant revisions to national clean energy policies to reduce emissions, including China’s target of 15% renewable electricity by 2020 [2], the European Union’s 2030 Energy Framework objectives, and India’s megawatts-to-gigawatts renewable energy commitment [3]. There is, however, a need to structure and integrate the industrial processes with advanced technologies for an efficient biomass recovery, and its conversion into products such as basic chemicals, platform chemicals, fuels and energy from economical and green alternative sources. In line with this, the scientific community has made significance efforts on sustainable growth and zero-waste industries by proposing several sustaining bio-energy processes [4–8]. Recently, black liquor derived as a waste residue in pulp and paper industry, has shown immense potential as a biomass based

https://doi.org/10.1016/j.suscom.2017.11.005 2210-5379/© 2017 Elsevier Inc. All rights reserved.

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Nomenclature A BFF ERS FFE GA hc H HSE k L m MSE N, i PH SC SE STE SNLAE T U V y

Heat transfer area (m2 ) Backward feed flow Energy reduction scheme Falling film evaporator Genetic Algorithm Enthalpy of condensate (kJ/h) Enthalpy of vapor (kJ/h) Heptads’ stage evaporator Liquor feed fraction Feed flow rate (kg/s) Vapor fraction sent to pre-heater Multiple Stage Evaporator Number of evaporative effects Pre-heater Steam consumption (kg/s) Steam economy Short tube evaporator Simultaneous nonlinear algebraic equation Vapor body temperature (◦ C) Overall heat transfer coefficient (kW/m2 ◦ C) Vapor flow (kg/s) Fresh steam split fraction

Subscripts i Effect number f Feed Greek letters  Latent heat of vaporization (kJ/kg)  Change/difference

source of energy in countries such as Sweden and Finland, where such an industry finds more prominence. Hence, it is of great interest to convert this byproduct from such industries to a high energy value carrier. As reported recently, globally this industry processes currently about 170 million tons of black liquor (measured as dry solids) per year, with a total energy content of about 2EJ, thereby, making it a very significant biomass source [9]. In comparison with other potential biomass sources for chemicals production, black liquor carries greater advantage as it is already partially processed and exists in a pumpable liquid form. Such a waste liquor could be used further, as a biofuel for the boiler, to generate steam that meets other process requirements in the same industry. The surplus amount of waste liquor may be converted from low-grade renewable energy to high-quality energy products such as carbon dioxide, methanol or dimethyl ether (DME) for automotive uses through Black Liquor Gasification (BLG) [10]. In contrast to the previously mentioned biomass utilizing countries, Indian pulp and paper mills rank 15th globally in terms of size, 6th in India and 4th among the most globally energy dependent industries, with energy needs of about 10 Mtpa of coal and 10.6 GW h of electricity [11]. A per ton paper consumption of steam of ∼11–15 tons and of electricity of ∼1500–1700 kW adds up to a huge annual average specific energy requirement of ∼52 GJ per ton of paper [12]. The consumption of fuel, primarily coal, required to achieve this energy requirement forms ∼15–20% of the total production cost which is significantly high compared to non-Indian pulp and paper mills [13–15]. Additionally, most of the energy is utilized in producing steam for various process units, specially the Kraft process, which is inherently responsible for consuming more than 24–30% of fresh steam needed for total plant [16–18]. An estimate reveals that for each ton of pulp produced, about 7 tons

of black liquor with 15% solids (∼10% organic and ∼5% inorganic chemicals) is produced [19]. This is equivalent to ∼13.5–14.5 MJ/kg solid of energy. To make such a liquor useful as a biofuel in the recovery boiler and to boost its energy recovery, this black liquor must be concentrated through Multiple-Stage Evaporator (MSE) to increase its solids content. This concentrated liquor has been described globally as a potential fifth most important fuel. The increasing costs of fuel/energy, high demands as well as stringent pollution regulations to control greenhouse gaseous emissions and water pollution have subjected Indian pulp and paper mills to tremendous pressures. Keeping this in mind, many researchers in the last decade have developed and proposed different Energy Reduction Schemes (ERS) to maximize Steam Economy (SE) and minimize Steam Consumption (SC) based on feeding mode of weak liquor and fresh live steam [20]. To achieve high energy efficiency of MSE, a Heptads’ Stage Evaporator (HSE) operated with Backward Feed Flow (BFF) installed in various paper mills nearby Saharanpur, U.P., India has been selected for the investigation. Such an operation predominantly used in many mills involves live fresh steam and weak black liquor moving counter-currently through various effects. For an accurate estimation of SE and SC, mathematical modeling and simulation is required to enable better process design and operation. A large number of steady state mathematical models of the MSE for different process industries have been proposed over last several decades. For example, El-Dessouky and Ettouney developed the mathematical model for the parallel feed MSE system with and without vapor compression [21]. The analysis concluded that the thermal performance ratio for thermal vapor compression system is higher at low brine temperatures and large number of MSE stages. Sharma and Mitra developed a model to predict the evaporative heat transfer coefficient in a horizontal tube-falling evaporator and applied it to evaluate overall performance of a desalination unit [22]. Bhargava et al., Khanam and Mohanty and Wu et al. developed the nonlinear steady state mathematical models with consideration of various ERS such as condensate-, feed- and product-flashing, and steam- and feedsplitting [23–25]. Xevgenos et al. developed a MSE simulator to predict the values of key operating parameters according to the specified inputs of the design variables [26]. The tool integrates mass and energy balances, process equipment parameters, cost estimation, and environmental aspects of the units involved. Ding et al. proposed a hybrid modeling approach to model two-phase flow evaporators [27]. Oh et al. investigated the heat transfer characteristics in a commercial-scale syngas cooler (SGC) consisting of a series of concentric helical coil channels [28]. For this, the detailed flow and heat transfer pattern in the unique heat exchanger were analyzed using computational fluid dynamics (CFD) for various operating loads and fouling conditions. These steady state models have been specifically designed and analyzed to improve the energy efficiency parameters namely SE and SC. Although, apparently there may be no need to supervise or to control the system at steady state condition, but this is not possible as flow rate of vapor, liquor and temperature of vapor at each effects keep frequently changing [29]. The reported steady state models have been classified on the basis of their inherent mathematical properties of linearity and nonlinearity. A set of linear algebraic equations have been developed, after making appropriate assumptions, to simplify solution for different ERS [30–33]. The advantage of such developed models is their linearity that provides faster, ease of simulation, and stability with desired convergence. The scope of linear developed models is, however, limited as pressure, Pi and temperature, Ti for each stage of MSE are fixed at plant data. However, the optimum operating process variables, Pi and Ti , that decide the optimum SE and SC may be different and generally unknown. In pursuit to find such optimum conditions, the linear mathematical model may

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translate into a more accurate nonlinear model. In many of the previous literature, nonlinear models for MSE have also been developed [16,34]. However, nonlinearity is the thumb rule, rather than an exception in most of the industrial processes. The nonlinearity occurred in the MSE is only due to dependence of one variable to other (such as Pi and Ti for each stage of MSE), and thereby, creating a complex nonlinear optimization problem. However, the solution of these nonlinear models has been attempted using one of the simplest analytical linearization method [35] and solved using numerical iterative approaches such as Gauss elimination method [36]. Such iterative methods are found to yield non-feasible results with the variation of operating conditions. Some such models have been derived earlier from thermodynamic first principles of mass and energy conservation for complex situations of MSE applied to milk, sugar and caustic evaporations. In order to optimize the nonlinear model operating parameters of MSE, number of approaches such as, sequential simplex optimization approach [37], pinch analysis [38,39], cascade algorithm [40] and exergetic analysis [41] have been explored in previous literature. Exergy analysis has been carried out earlier through such steady-state nonlinear model development to locate points (here evaporation stage number) and quantity of energy degradation across a fourstage evaporative process [41]. This helped to identify an exergetic improvement potential across different evaporation stages. Likewise, integration of the MSE with given effect temperatures with the background process has been proposed for a corn glucose process through such a nonlinear model development after making a grand composite curve in pinch analysis [42]. This yielded optimal effect temperatures for maximum energy recovery. However, both the methodologies yield a set of simultaneous nonlinear algebraic equations (SNLAEs). The solution of single nonlinear equation has been demonstrated earlier to be straight forward through solution of a single cubic polynomial model of MSE using cascade algorithm [23]. The solution of SNLAEs, however, has been found to be quite challenging [43], and becomes even more complex especially, when the number of effects increases in MSE leading to a higher number of equations to solve. However, there are no generalized methods to solve SNLAE. The solution of linearized models and SNLAE has been achieved earlier using classical numerical iterative techniques namely, Gauss Elimination [35], Secant and Newton’s methods [34,42,44], etc. It has been observed that an iterative method only works for a given operating ERS and fails for others, which is primarily due to a change in entire set of governing equations [36]. Multivariable Newton-Raphson method is a better option when the SNLAE may yield analytical partial derivatives of variables with respect to all remaining variables. However, Newton-Raphson method suffers from three prime problems: the estimation of the partial derivatives to compute the Jacobian matrix is quite exhaustive, the solution shows dependency on the initial guess offered and the solution shows the divergence. Hence, the solution of SNLAE exhibit instability and diverging results if the initial boundary solution estimates provided is far away from its true estimated. An example is the solution of the problem of propane combustion in air modeled earlier as a set of five SNLAE that yielded four solutions using Newton-Raphson method but would otherwise actually have one global optimal and unique solution. Grosan and Abraham proposed a new prospective evolutionary technique to solve such complex nonlinear equations problems [45,46]. The theory is based on representation of every equation in the system as an objective function. The aim of each such function is to optimize the difference between the left and right terms of the corresponding equation. In the backdrop of aforementioned limitations of the iterative and Newton’s method in solving SNLAE, it appears that there is an enormous scope to propose and employ better solution techniques for MSE which can accommodate usual encountered problems of

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divergence and initial guess dependency. Hence, the present work addresses the problem of solution of complex SNLAE appearing in MSE modeling through the use of nature inspired Genetic Algorithm (GA). In this work, seven process operations or ERS for a backward-feed flow (BFF) heptads’ stage MSE shown in Table 1 for the Indian paper mill, have been modeled. A typical sketch for heptads’ stage MSE with BFF configuration with these added process arrangements has been illustrated in Fig. 1. The models have emanated from first principle of thermodynamics involving mass and heat balance basic fundamentals described earlier [18,32,47–50]. The developed mathematical models for MSE constitute a complex set of SNLAE that require to be solved. Karr et al. demonstrated a new technique to evaluate the solution of SNLAE’s using GA and Vasconcelos GA or VGA methodologies [51]. Sarkar and Modak proposed a technique for solving multi-objective optimal control problems under the framework of Non-dominated Sorting Genetic Algorithm (NSGA) and its enhance version NSGA-II [52]. Ramteke and Gupta utilized the elitist (NSGA-II) and Multi-Objective Simulated Annealing (MOSA) with the robust fixed-length jumping gene adaptation (aJG) technique in order to solve such intensive multi-objective optimization problems for an industrial semi batch nylon-6 reactor [53]. Further, Sharma et al. optimized the design of a falling film evaporator system for dairy industries to concentrate milk by developing a user friendly program on MS-Excel to solve multi-objective problem [54]. The methodology for developing the program has been based on NSGA-II. Joshi and Bala Krishna demonstrated the effectiveness of GA to solve travelling salesman, tank reactor and neurophysiology complex nonlinear models [55]. Ramteke and Gupta and Ramteke et al. proposed Real-coded elitist Non-dominated Sorting Genetic Algorithm (RNSGA-II), adapted from the NSGA-II [56–58]. Sarkar and Modak proposed a solution strategy based on GA approach for the determination of optimal substrate feeding policies for bed-batch bioreactors with two control variables [59]. The determination of optimal feed rate profiles for fed-batch bioreactors with two feed rates is a numerically difficult problem involving two singular control variables. However, important aspect of GA is the capability of representation of the decision variables to represent a substrate feeding profile to the bioreactor. However, numerical techniques can’t be able to formulate such an optimization problem that can determine the feed rate for both the substrates during the entire period of operation. Verma et al. simulated the dynamic behavior of heptads’ stage MSE for base case configuration (here, Model-BF1) in similar condition [60]. The analysis of dynamic behavior has been explored by disturbing the flow rate, concentration and temperature of feed, and fresh steam flow rate around the steady state values. These steady state values have been evaluated using I-PM. Hence, on the basis of these pertinent previous literature, GA approach has been employed in the present work in order to compute these steady state values for the evaluation of the most important deciding factor, SE and SC.

2. Nonlinear mathematical modeling of MSE for solution of SNLAE using Genetic Algorithm (GA) If the black liquor is fed to the last stage of MSE and steam to the first stage, the generated configuration is backward feed (BF) arrangement. The unconcentrated weak black liquor moves counter current to fresh steam supplied, i.e. from last to first stage, and the concentrated product is received from the first stage. The vapor obtained at each stage is further utilized as a re-heat source for the next successive stage. The Model-BF1 refers to the base case of BFF configuration represented in Fig. 1 (through black solid line). Here, the vapor produced, V2 , at first stage is sent directly to the second stage as a heat source

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Table 1 Description of proposed model configurations. Model Name

Process configuration characteristics

Backward-feed (Model-BF1) Steam-split backward-feed model (Model-BF2)

Fresh steam entered at first stage and weak liquor entered at last stage. Steam is split among first two stages with split fractions, Y in first, and (1-Y) in second stage and liquor fed to seventh stage. Fresh steam entered at first stage and weak liquor entered at last two stages, sixth and seventh with feed spilt fraction, K, and (1-K) respectively. Fresh steam split among first two stages with split fraction, Y, and (1-Y) and weak liquor split among last two stages with split fraction, K, and (1- K) respectively. Fresh steam entered at first stage and weak liquor entered at last stage; two preheaters installed to preheat entering weak black liquor before feeding to MSE (PH-1 and PH-2); m fraction of vapor sent from seventh and sixth stage to preheaters for feed preheating. Fresh vapor split among first two stages with split fraction, Y, and (1-Y) respectively. Preheaters used to concentrate the entering feed. Liquor split to last two stages with feed-split fraction, K, and (1-K), and steam split to first two stages with split fraction, Y, and (1-Y). Preheaters used to concentrate the feed liquor with fraction, m, of vapor sent to preheaters from last two stages.

Feed-split backward-feed model (Model-BF3) Feed- and steam-split coupled backward-feed model (Model-BF4)

Feed preheating backward-feed model (Model-BF5)

Steam-split and feed-preheating coupled backward-feed model (Model-BF6) steam-, feed-split and feed preheating coupled backward-feed model (Model-BF7)

Fig. 1. Illustration of a backward-feed based heptads’ stage MSE with steam-split, feed-split and feed preheating as Energy Reduction Schemes (ERS).

(in spite of steam-split among first two stages) and that produced from third stage is sent to the fourth as a heat source, and so on. No other operation such as feed-split, steam-split and black liquor preheating is considered for this base model. The described MSE model shown in Fig. 1 is developed after employing the thermodynamic first principle across each stage. Each stage constitutes of two equations (mass and energy balance), one for the evaporator side, and another one across the steam chest. As illustrated in Fig. 1, live steam enters the first stage of MSE with a flow rate V1 and at temperature T1 , and exits it as condensate. As the result of evaporation of water in the first stage, steam or vapor generated is dragged to the steam chest of the second effect and so on. The vapors from seventh stage move to a vacuum pump or a steam ejector or a barometric condenser. This arrangement provides first stage operation at maximum pressure (or maximum temperature), and the last stage at minimum pressure (i.e. minimum temperature). Applying the mass and energy balance for the first stage, we have-

[Liquor flow or energy entering the stage from second stage with sensible heat] + [Steam flow or energy entering the vapor chest with latent heat] = [Vapor flow or energy leaving the stage with latent heat] + [Liquor flow or energy leaving the stage with sensible heat] V1 1

+ L2 h2

=

+ L1 h1

V2 H2

(1)

Since, V1 = L2 − L1 , Eq. (1) modifies toV1 1

+ L2 h2

=

(L2 − L1 ) H2

+ L1 h1

(2)

Hence, Eqs. (3) and (4) represents nonlinear algebraic equations around the first effect f1 ≡

V1 1

+ L2 h2

−

(L2 − L1 ) H2

− L1 h1 = 0

f 2 ≡ U 1 A1 (T 1 − T 2 ) − V 1 1 = 0

(3) (4)

Similarly, the rest of the 12 nonlinear equations for the second to seventh stages may be defined as below: For the second stage, f3 ≡

(L2 − L1 ) 2

+ L3 h3 − (L3 − L2 ) H2

− L2 h2 = 0

(5)

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f4 ≡ [U2 A2 (T2 − T3 )] − [(L2 − L1 ) 2 ] = 0

(6)

For the third stage, f5 ≡

(L3 − L2 ) 3 + L4 h4 − (L4 − L3 ) H3

− L3 h3 = 0

f6 ≡ U3 A3 (T3 − T4 ) − (L3 − L2 ) 3 = 0

(7) (8)

For the fourth stage, f7 ≡

(L4 − L3 ) 4 + L5 h5 − (L5 − L4 ) H4

− L4 h4

=0

f8 ≡ U4 A4 (T4 − T5 ) − (L4 − L3 ) 4 = 0

(9) (10)

For the fifth stage, f9 ≡

(L5 − L4 ) 5 + L6 h6

− (L6 − L5 ) H5 − L5 h5 = 0

f10 ≡ U5 A5 (T5 − T6 ) − (L5 − L4 ) 5 = 0

(11) (12)

For the sixth stage, f11 ≡

(L6 − L5 ) 6 + L7 h7 − (L7 − L6 ) H6

+ L6 h6

=0

f12 ≡ U6 A6 (T6 − T7 ) − (L6 − L5 ) 6 = 0 For the seventh stage, f13 ≡





(L7 − L6 ) 7 + Lf hf − Lf − L7 H7 − L7 Cp T7

(13) (14)

=0

f14 ≡ U7 A7 (T7 − Tf ) − (L7 − L6 ) 7 = 0

(15) (16)

Eqs. (1)–(16) represent a set of fourteen SNLAE equations which are a combination of linear and nonlinear equations. The solution of which using GA is shown in Section 3. In the steam-split based Model-BF2 configuration, the fresh steam with flow rate, V1 , is split among first and second stages with split fractions, Y and (1-Y), respectively, illustrated in Fig. 1 (through an orange short-dash line). The steam produced from first and second stages is collectively sent to the third stage as a heat source, while the vapor produced from the third stage is sent to fourth stage, and so on. The equations for third − seventh stages remain the same as for the previous model but the equations for the first two stages change due to inclusion of steam-split operation. For this model, the equations derived for first and second stages are shown by Eqs. (17)–(20). The equation for the last stage is similar to Eqs. (15) and (16), presented earlier. For the first stage, Y (L2 − L1 )1 + L2 h2 − L1 h1 − (L2 − L1 )H2 = 0

(17)

U1 A1 (T1 − T2 ) = Y (L2 − L1 )1

(18)

The next proposed model is Model-BF4, wherein both the steam-split and feed-split arrangements are incorporated into the base Model-BF1. The mathematical equations for the first two stages remain the same as in previous explained operations due to the steam-split operation (similar to in Eqs. (3) and (4) derived for Model-BF2). Likewise, for the last two stages the equations are similar (as in Eqs. (23) and (24) derived for Model-BF3) due to the liquor feed-split. For the remaining effects third to fifth, the equations are equivalent to Eqs. (7)–(12) derived for the Model-BF1. Model-B5 represents the basic model of BF feed flow with added preheaters, PH-1 and PH- 2. The black liquor to be fed at the last stage is preheated before being sent to MSE using the two preheaters as illustrated in Fig. 1 (dash-dot brown line). Such an introduction of two preheaters has been attempted previously [30] in which some part of vapor obtained from sixth stage was proposed to be sent to the last stage, i.e. seventh stage, and the remaining to the first preheater (PH-1). The vapor from the last stage, i.e. seventh stage, is completely sent to the preheater (PH-2) as a heat source. In the present work, similar type of arrangements have been incorporated for feed-preheating. The vapor produced at sixth stage is split into fraction, m, (varied from 0–90%) with mV7 amount sent to preheater PH-1 and the rest sent as a heat source to the last stage. The vapor produced at the last stage is sent to PH-2 for preheating the black liquor coming from PH-1. Hence, the unconcentrated black liquor temperature increases by TPH−1 (=T − T0 ) in PH-1 and by TPH−2 (= T − T1 ) in PH-2. The unconcentrated black liquor is then fed to the last stage, and the concentrated product obtained from the first stage. The mathematical expression for Model-BF5 remains the same for the first to fifth stages as for the base case of backward-feed. However, for the sixth and seventh stages the derived mathematical expressions are represented by Eqs. (25) and (28). (L6 − L5 )6 + L7 h7 + {(1 − K)Lf hf } − L6 h6 − {L7 +(1 − K)Lf − L6 }H7 = 0

U6 A6 (T6 − T7 ) = (L6 − L5 )6

(1 − Y )(L2 − L1 )1 + L3 h3 − L2 h2 − (L3 − L2 )H3 = 0

(19)

U2 A2 (T2 − T3 ) = (1 − Y )(L2 − L1 )1

(20)

In another feed-split model, Model-BF3, the liquor fed to the last stage is split between sixth and seventh stages with split fractions, K and (1-K), as represented in Fig. 1 (Medium-dash line), and with all other conditions remaining the same as in base Model- BF1 configuration. Therefore, the mathematical equations are similar to those for first to fifth stages of Model-BF1. However, the equations for the sixth and seventh stages get modified, and are expressed by Eqs. (21)–(24). For the sixth stage, (L6 − L5 )6 + L7 h7 + KLf hf − L6 h6 − (KLf + L7 − L6 )H7 = 0

(21)

U6 A6 (T6 − T7 ) = (L6 − L5 )6

(22)

For the seventh stage, (L7 − L6 )7 + (1 − K)Lf hf + {L7 − (1 − K)Lf }H8 − L7 h7 = 0

(23)

U7 A7 (T7 − T8 ) = {(1 − K)Lf − L7 }7

(24)

(25)

(26)

{L7 + (1 − K)Lf − L6 }7 + KLf hf − (KLf − L7 )H8 − L7 h7 − Lf Cp T1 = 0

U7 A7 (T7 − T8 ) = {L7 + (1 − K)Lf − L6 }7 − Lf Cp T1

For the second stage,

5

(27)

(28)

In Model-BF6, the steam-split and feed preheating operations are incorporated into Model-BF1 configuration. Here, the fresh steam is proposed to be split amongst the first two stages with split fraction, Y, going to the first stage and remaining fraction, (1-Y), to the next stage, i.e. second stage. Two preheaters, PH-1 and PH-2, preheat the feed before it is fed to MSE. A fraction m of the vapor produced at sixth stage is fed to PH-1, and all the vapor produced from seventh stage sent to PH-2, with a view to best utilize the heat leaving the system. The Model-BF6 equations for the first two stages are similar to that of Model-B2 due to similar steam-split operation and are expressed by Eqs. (17)–(20). For the stages third to fifth, the model equations remain identical to equations for Model-BF1 (in Eqs. (7)–(12)). For the last two stages, the derived model equations are similar to those of Model-BF5 represented earlier by the Eqs. (13)–(16). Finally, Model-BF7 combines all the operations mentioned earlier: a steam-split operation as in Model-BF2 with the split fractions, Y and (1-Y), a feed-split operation as in Model-BF3 with split fractions, K and (1-K), and feed-preheating (using PH-1 and PH-2) as in Model-BF5 with vapor split fractions, m and (1-m). Therefore, the

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Table 2 Operating parameters of heptads’ stage MSE. S. N.

Parameter (S)

Value (s)a

1. 2.

Total number of stages Live steam temperature for Model-B2, Model-B6 and Model-B7 Inlet black liquor concentration, Xf Outlet black liquor concentration, Xp Inlet liquor temperature, Tf Black liquor feed flow rate, LF Last stage (Seventh) vapor temperature, T7 Feed flow configuration Heat transfer area (A1 -A2, A3 -A6 and A7 )

07 140◦ C (Stage-1) 147◦ C (Stage-2) 0.118 0.54 62◦ C 56,200 kg/h 52◦ C Backward Feed (BF) 540 m2 each, 660 m2 each and 690 m2

3. 4. 5. 6. 7. 8. 9.

a All the data in table have been acquired from a nearby paper mill, Saharanpur, U.P, India.

equations derived for the model are partly similar to these previous models, and hence, are self-explanatory. 3. Solution of developed SNLAE model using Genetic Algorithm (GA) Yang proposed that advanced stochastic algorithms may be considered a type of metaheuristics algorithms [61]. Evolutionary Algorithms (EA) may be found as a better option when compared to conventional numerical techniques such as Newton Raphson and Gauss elimination method [62]. For the heptads’ stage MSE, the nonlinear model developed for each configuration consists of a 14-set of SNLAE, and hence, yields a 14 × 14 Jacobian matrix. Each element of this Jacobian matrix consists of analytical derivatives of few variables with respect to all the variables. The computational complexity involved in determining such analytic derivatives makes the Jacobian matrix a highly complex one, and hence, the problem solution very painful. Newton Raphson method is associated with other problems such as the need to assume an initial starting boundary in proximity to true solution. If the initial boundary is far away from the true solution, then the MSE may exhibit instability and diverging results. EA may prove an important numerical optimization gadget based on its practical approach to obtain global optimal for real world problems like nonlinear, non-differential, continuous, discontinuous as well as real time problems [63]. This technique does not need any other information about the mathematical model such as its differentiability. However, it requires an adequate initial boundary value(s) to start with. The rigidity in having such a starting point close to the true solution is not so high compared to that of Newton-Raphson method.

Ti

>

Ti+1 , ∀ {i = 1, 2, . . . . 5} and T6

Li

<

Li+1 , ∀ {i = 1, 2, . . . . 7}

>

U is made. A complete algorithm illustrating steps performed for achieving the true solution is schematically using the flow chart shown in Fig. 2. The proposed solution algorithm illustrated in Fig. 2 makes a comparison of Unew with the Uold to take a decision. This Unew has been evaluated using of temperature and flow rates determined for first to last stage. If the difference in estimate of U between two successive iterations falls within 40% at each stage, only then SE is computed else this procedure is repeated until it falls within the mentioned threshold [36]. 3.1.1. Genetic Algorithm (GA) based approach Ahn et al. implemented GA approach to determine the shortest path (SP) routing problem [64]. However, for solving SNLAE’s involving constrained and unconstrained nonlinear optimization problem, Grosan and Abraham, Pourrajabian et al., Malik et al. and Kuri-morales have demonstrated the effectiveness of GA for different applications such as nonlinear biochemical reaction system, combustion application problem, neurophysiology application problem [43,46,65,66]. The present algorithm starts with generations of random chromosomes and initial population set within the specified range. The chromosomes, positioned in population set, are a bunch of the 14-unknown variables for heptads’ stage, i.e. Li and Ti in the present investigation. Thus, the population size becomes 15× no. of unknown variables (14), i.e. 210. The specific boundary conditions have been collected from paper mill near by Saharanpur, U.P, India and used as initial boundary values for the GA which are presented later using Eqs. (33)–(35). In the current optimization problem, the focus has been primarily to maximize the SE and minimize SC. The assumed cost function (SE) is hereby expressed mathematically through Eq. (29). Maximize f 1 (x) ⇒ Minimize {−f 1 (x)}

(29)

f1 returns the maximum of SE and minimum of SC for MSE. Mathematically, SE is defined by Eq. (30). 8 

f1 (x) = SE =

Vi

i=2

(30)

V1

f1 is a strong function of decision variables, x, which are: the amount of fresh steam supplied, V1 , and amount of vapor produced, Vi (i ∈ {2; 7}) . These vapor flow rates, Vi (i ∈ {2; 7}), are functions of liquor flow rates, Li (i ∈ {1; 7}), and vapor temperatures at each effect, Ti (i ∈ {1; 6}) . For the proposed operations, we make the following assumptions in the feasible region of decision variables, x. The inequality constraints, g(x) > 0 , are defined such that: 52◦ C (T7 ), Ti

>

0, ∀{i = 1, 2, . . . . 6}



(31)

and Li > 0, ∀{i = 1, 2, . . . . 7}

In present work, GA has been employed to compute the SNLAE true solution. 3.1. Solution Algorithm for SNLAE-MSE models To evaluate the true solution of the developed benchmark SNLAE models (Model-B1to Model-B7) of MSE, an iterative procedure using GA has been employed in detail. The set of model equations, with the available system information (Table 2), are computed using a model specific MATLAB source code simulated at Intel(R) Xeon(R) [email protected] GHz workstation with a 32.0 GB RAM. For starting the iterative calculation, an initial approximation of

The condition for temperature bounds for the given BFF configuration is chosen to realistically match the situation of a gradual decrease in temperature Ti from the first to the last effect. The temperature of last effect has been kept fixed at 52 ◦ C based on available plant operating data presented in Table 2. Similarly, the liquor flow rate decreases from seventh to first effect due to an increase in its concentration, and the same has been incorporated in Eq. (32). Likewise, the equality constraints, hi (x) = 0, are based on the concept of setting the mass and energy balances to zero, i.e., (Li − Li−1 )i + Li+1 hi+1 − Li hi − (Li+1 − Li )Hi+1 = 0 Ui Ai (Ti − Ti+1 ) − (Li − Li−1 )i = 0



(32)

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For vapor temperature and black liquor flow rates, the chosen feasible bounds using real-time operating data from Indian pulp and paper mill for the proposed models are detailed below for Model-B1, Model-B2 and Model-B5, Ti

∈

[100 : 110; 70 : 85; 66 : 74; 60 : 70; 55 : 65; 52 : 63] ∀ {i =

Ti

∈

[115 : 125; 113 : 125; 66 : 74; 60 : 70; 55 : 65; 52 : 63] ∀ {i =

and,Li

∈

⎫ ⎪ ⎬

2, 3, . . . ., 7} 2, 3, . . . ., 7} respectively,

[2 : 5; 3.5 : 6; 4.5 : 7; 6.5 : 9; 9 : 11; 10.5 : 13; 13 : 15] ∀ {i =

1, 2, . . . ., 7}

For Model-B3 and Model-B4, 55 : 65; 52 : 63] ∀ {i =

Ti

∈

[100 : 110; 70 : 85; 66 : 74; 60 : 70;

Ti

∈

[115 : 125; 113 : 125; 66 : 100; 60 : 70; 55 : 95; 52 : 85] ∀ {i =

and,Li

∈

[2 : 5; 3.5 : 6; 4.5 : 7; 6.5 : 9; 9 : 11.5; 6 : 8; 6 : 8] ∀ {i =

2, 3, . . . ., 7} respectively,

1, 2, . . . ., 7}

Li

∈

[2 : 5; 3.5 : 6; 4.5 : 7; 6.5 : 9; 9 : 11; 10.5 : 13; 13 : 15] ∀ {i =

Li

∈

[2 : 5; 3.5 : 6; 4.5 : 7; 6.5 : 9; 9 : 11; 6 : 8; 6 : 8] ∀ {i = 1, 2, 3, . . . ., 7} respectively,

and,Ti

∈

1, 2, 3, . . . ., 7}

[115 : 125; 113 : 125; 66 : 115; 60 : 100; 55 : 95; 52 : 85] ∀ {i =

For the steam demand, the selected feasible bound is V1 ∈ [0:3]. The formulated optimization model is further solved using (IPM) with a computer program made in the MATLAB environment, using the process parameters information available from plant tabulated in Table 2. The offset of SNLAE as objective functions for all the chromosomes has been evaluated, and the fittest among them is chosen as a parent to produce new generations (offsprings) and the others are discontinued. Usually, selection has been made through a tournament that helps to identify a set of chromosomes with lowest cost, which becomes parents for the new generations. The next step is crossover that is employed after selection, in which, crossover operator combines substructures of two parent chromosomes to produce a new structure known as child. The crossover fraction probability utilized in present optimization problem is 0.8. To attain an appropriate relevant solution, mutation operations have been employed to bring random variations in the genes to maintain the genetic diversity. This avoid the solution convergence to a local optimum, and hence, forces the algorithm to exhaustively explore variables space to attain the global optimum.

⎫ ⎪ ⎬

2, 3, . . . ., 7}

For Model-B6 and Model-B7,

2, 3, . . . ., 7}

(33)

⎪ ⎭

(34)

⎪ ⎭

⎫ ⎪ ⎬ (35)

⎪ ⎭

Migrations occurs only if the vector length population size exceeds one during the crossover and mutations. The best individuals from one sub-population replace the worst individuals in another subpopulation during migrations. Individuals that migrate from one sub-population to another are copied, but not removed from the source sub-population. Here, migration interval and fraction are taken as 20 and 0.2, respectively. The number of generations chosen for the present work is 200× no. of variables (14) i.e. 2800. The last step of GA technique is its stopping criterion. The algorithm ends if the chosen maximum number of generations is exceeded or the constraint parameter U, has converged, else the evaluation, selection, reproduction and mutation are repeated. Grosan and Abraham have explained GA approach mathematically through a schematics similar to that shown in Fig. 3 and applied the same in a useful optimization problem [46].

Fig. 2. Schematics of solution algorithm for solving the proposed MSE models.

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Fig. 3. Schematics of GA procedure used to solve the developed MSE model.

4. Results and discussion Various configurations of a backward-feed based heptads’ stage MSE with different ERS (steam-split, liquor feed-split, black liquor preheating and their hybrids) have been modeled and presented earlier in Section 3. GA has been used to optimize the cost function, SE, in the present work, thereby, yielding optimal values of process parameters, Ti , Li and V1 . For different operating conditions searched by GA for the maximum energy efficiency, SE has been evaluated for different feasible bounds of Ti ’s, (i = 2–7) and Li ’s (i = 1–7). Optimum solution for SE and SC are presented in Table 3. It may be noted that split-fractions (Y, K and m) mentioned in the table refer to their optimal values which provide maximum SE. Additionally, optimum process parameters namely flow rate of liquor, temperature of vapors produced, and estimates of other thermo-physical properties have also been presented for suitable comparison of different models. The results presented in Table 3 indicate that the base case of BF configuration without any added operations (Model-BF1) provides a SE of 3.65 and SC of 10800 kg/h. Addition of steam-split operation to BFF configuration (Model-BF2) improves the SE to 5.13 (a 40.55% increase) and decreases SC to 7956 kg/h (a 26.33% decrease). Interestingly, the addition of feed-split operation to backward-feed configuration (Model-BF3) improves the SE drastically by 44.11% to 5.26 and decreases SC by 34.30% to 7096 kg/h. The addition of feed-preheating operation to BF configuration (Model-BF5), however, improves the SE by 40.27% to 5.12 and decreases SC by 26.06% to 7985.94 kg/h. On a comparative basis, the mentioned results indicate that the addition of feed-split operation to the backwardfeed based process design has independently a marginally higher influence on the energy efficiency than other energy optimization strategies, namely steam-split and feed-preheating. The splitting of steam among first two stages and sending the combined vapors from these stages to the third stage and further translates into improved heat transfer rates in the later stages. The addition of the steam-split and feed-split operations to base BF configuration

(Model-BF4), yields SE of 5.95 and SC of 8316.45 kg/h. These values are improved and are better than backward feed operation by 63.30% and 23%. Further, the values are even better when compared individually to the separate addition of steam-split (Model-BF2) and feed-split (Model-BF3) to the backward flow configuration (Model-BF1) where the maximum improvements observed are 16% and 13.12%, respectively in SE. Addition of the steam-split and feedpreheating operations to the base BF yields SE of 5.17 and SC of 7866.58 kg/h for Model-BF6. These values are improved and are better than backward feed operation by 41.64% and 27.16%. Addition of steam-split, feed-split and feed preheating operations to the base BF configuration results in SE and SC of 6.47 and 6541.93 kg/h, respectively, for Model-BF7. Apparently, compared with the base BFF configuration (Model-BF1), SE and SC improve significantly by 77.26% (increase) and by 39.43% (decrease), respectively. Based on the results presented in this section, it is quite interesting to note that the use of combined three options (steam-split, feed-split and feed-preheating) yields the maximum SE and minimum SC. Hence, comparatively Model-BF7 based design configuration provides a better process operating strategy as compared to others. The proposed ERS models (Model-BF1 to Model-BF7) have been simulated for varying split ratio from 1% to 90%. However, split fractions (Y, K and m) represented in Table 3 signifies the optimal values that may yield maximum SE. Finally, a comparison of the simulation results of the presented models with previously published articles [24,32,67] is presented in Table 4. The previous investigators have worked with similar type of MSE system for concentrating black liquor for Indian pulp and paper industries at same process conditions but with different approaches and algorithms to find optimum SE and SC. It is observed that GA helps to search for better optimal values of parameters that yield higher SE and lower SC. It may be noted that SE and SC values for configurations modeled in this work are significantly improved by (36.2% and 32% respectively) than the reported values earlier.

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Table 3 GA simulation results of the proposed model configurations of backward-feed based heptads’ stage MSE*. Model Description

Estimated Process Parameter

Stage Number 1

2

3

4

5

6

7

Exit/Avg.

ModelBF1 (Base case)

Flow rate of liquor, kg/h Temperature of vapors produced, ◦ C Latent heat of vaporization, kJ/kg Enthalpy of vapor, kJ/kg Steam economy Steam consumption, kg/h

16732.81 147.00 2109.51 2748.03 3.65 10800

21600.00 100.00 2251.53 2678.37

23569.86 85.00 2293.27 2653.98

30217.57 73.99 2322.80 2635.41

36761.42 68.40 2337.44 2625.76

43005.22 63.58 2349.86 2617.33

49035.05 57.1 2366.28 2605.82

56196* 52* 2379* NA

Model-BF2 (steam-split), (Y = 0.9)

Flow rate of liquor, kg/h Temperature of vapors produced, ◦ C Latent heat of vaporization, kJ/kg Enthalpy of vapor, kJ/kg Steam economy Steam consumption, kg/h

15385.14 147.00 2109.51 2748.03 5.13 7956

21600.00 100.00 2251.53 2678.37

22209.50 85.00 2293.27 2653.98

28918.84 74.00 2322.77 2635.42

35614.45 68.53 2337.10 2625.99

42096.03 63.72 2349.50 2617.58

48498.03 57.25 2365.91 2606.09

56196* 52* 2379* NA

Model-BF3 (Feed-split), (K = 0.9)

Flow rate of liquor, kg/h Temperature of vapors produced, ◦ C Latent heat of vaporization, kJ/kg Enthalpy of vapor, kJ/kg Steam economy Steam consumption, kg/h

13954.29 147.00 2109.51 2748.03 5.26 7096

19819.80 100.00 2251.53 2678.37

25200.00 75.52 2318.75 2638.02

30305.86 66.00 2343.64 2621.58

32400.00 60.00 2358.97 2611.00

28800.00 58.13 2366.36 2605.77

28800.00 57.07 2251.53 2678.37

56196* 52* 2379* NA

Model-BF4 (Steam-split and feed-split) (Y = 0.1, K = 0.2)

Flow rate of liquor, kg/h Temperature of vapors produced, ◦ C Latent heat of vaporization, kJ/kg Enthalpy of vapor, kJ/kg Steam economy Steam consumption, kg/h

14867.98 147 2109.51 2748.03 5.95 8316.45

21600.00 100.00 2251.53 2678.37

23168.61 83.06 2298.54 2650.75

29623.22 66.33 2342.79 2622.15

32400.00 60.00 2358.97 2611.00

28800.00 56.67 2367.36 2605.05

28800.00 57.96 2364.12 2607.36

56196* 52* 2379* NA

Model-BF5 (Feed preheating) (m = 0.1)

Flow rate of liquor, kg/h Temperature of vapors produced, ◦ C Latent heat of vaporization, kJ/kg Enthalpy of vapor, kJ/kg Steam economy Steam consumption, kg/h

7985.94 147 2109.51 2748.03 5.12 7985.94

15338.79 100.00 2251.53 2678.37

21600.00 85.00 2293.27 2653.98

22183.23 74.00 2322.77 2635.42

28952.76 68.41 2337.41 2625.78

35724.48 63.49 2350.09 2617.17

42278.32 56.82 2366.99 2605.32

56196* 52* 2379* NA

Model-BF6 (Steam-split and feed preheating), (Y = 0.1, m = 0.9)

Flow rate of liquor, kg/h Temperature of vapors produced, ◦ C Latent heat of vaporization, kJ/kg Enthalpy of vapor, kJ/kg Steam economy Steam consumption, kg/h

7866.58 147 2109.51 2748.03 5.17 7866.58

15529.29 100.00 2251.53 2678.37

21600.00 85.00 2293.27 2653.98

23255.12 74.00 2322.77 2635.42

31466.63 67.77 2339.07 2624.67

39600.00 62.25 2353.26 2614.98

46717.63 55.76 2369.64 2603.42

56196* 52* 2379* NA

Model-BF7 (Steam-split, feed-split and feed preheating) (Y = 0.9, K = 0.6, m = 0.8)

Flow rate of liquor, kg/h Temperature of vapors produced, ◦ C Latent heat of vaporization, kJ/kg Enthalpy of vapor, kJ/kg Steam economy Steam consumption, kg/h

6541.93 147 2109.51 2748.03 6.47 6541.93

13865.13 105.38 2236.14 2686.86

19545.90 82.76 2299.35 2650.24

25200.00 73.04 2325.30 2633.78

31175.53 67.88 2338.78 2624.86

37356.05 63.21 2350.81 2616.68

43437.70 56.67 2367.36 2605.05

56196* 52* 2379* NA

Table 4 Comparison of GA results of proposed Model (Model-BF1–BF7) with previously reported models and analysis for similar MSE configurations. Model Description

SCa (in kg/h)

% reduction in SC

SEa

% improvement in SE

Real plant data estimates

Star paper mill, Saharanpur, India

9612

–

4.53–4.75b

–

Previously reported simulated output

Bhargava et al. model Khanam & Mohanty model Verma et al. linear model

8784 8712 9113.27

8.6 9.3 5.18

5.15 5.56 4.82

13.7 22.7 6.41

Simulated output using GA

Model-BF1 Model-BF2 Model-BF3 Model-BF4 Model-BF5 Model-BF6 Model-BF7

10800 7956 7096 8316.45 7985.94 7866.58 6541.93

−12.4 17.2 26.2 13.5 17 18.2 32

3.65 5.13 5.26 5.95 5.12 5.17 6.47

−23.15 8 10.7 25.3 7.8 8.8 36.2

a b

Available plant data estimates in ideal condition when no fouling has been considered. The SE has been reported in a range that considers the extreme values found between fouling and non-fouling conditions.

The Table 4 compares the simulated SE and SC results obtained using GA. It is observed that proposed methodology, GA shows better efficiency through improved SE to solve such nonlinear models compared to other numerical, optimization and pinch analysis reported previously reported [24,32,67]. This may be attributed to

an improved search of operating parameter (temperature, pressure and liquor flow rate at each stage) envelope for optimized conditions by GA. The steady-state analysis indicates that the search technique GA, could influence strongly the determination of opti-

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mized process conditions, and hence, ensuring energy savings for MSE. 5. Summary and conclusions In this work, seven ERS models have been proposed and solved using GA as part of the modeling and simulation of backward-feed heptads’ stage MSE. The present investigation demonstrates that GA efficiently evaluates the solution of complex nonlinear system of equations derived for the considered system. The simulations for different model configurations indicate that the configuration that utilizes all the ERS viz. steam-split, feed-split and feed-preheating, results in an optimal process design by providing highest SE and minimum SC. Interestingly, GA predicts that a highest SE of 6.47 and minimum SC of 6542 kg/h is possible if the MSE is operated with a steam-split fraction of 0.9, feed-split fraction of 0.6 and vapor-split fraction of 0.8. These estimates are much higher than the estimates reported previously from simulation (5.15–5.56) and the real plant statistics (4.53–4.75). Additionally, it has been observed that feedsplit based ERS provides a higher improvement in energy efficiency (SE = 5.26) as compared to steam-split (SE = 5.13) and feed preheating (SE = 5.12) operations. It is proposed, that the GA technique shall show even more superiority to similar process modeling problems where the number of nonlinear equations could be even higher than present case (14). References [1] International Panel on Climate Change (IPCC), Climate Change 2014 Synthesis Report Summary Chapter for Policymakers, 5th Assess. Rep. (2014) 31. 10.1017/CBO9781107415324. [2] World Resources Institute, The network for climate and energy information renewable energy in China: an overview, Renew. Energy (2008) 1–2. [3] U.S. Energy Information Administration, International Energy Outlook 2016, 2016 www.eia.gov/forecasts/ieo/pdf/0484(2016).pdf. [4] H.J. Huang, S. Ramaswamy, W.W. Al-Dajani, U. Tschirner, Process modeling and analysis of pulp mill-based integrated biorefinery with hemicellulose pre-extraction for ethanol production: a comparative study, Bioresour. Technol. 101 (2010) 624–631, http://dx.doi.org/10.1016/j.biortech.2009.07. 092. [5] E.A.B. da Silva, M. Zabkova, J.D. Araújo, C.A. Cateto, M.F. Barreiro, M.N. Belgacem, A.E. Rodrigues, An integrated process to produce vanillin and lignin-based polyurethanes from Kraft lignin, Chem. Eng. Res. Des. 87 (2009) 1276–1292, http://dx.doi.org/10.1016/j.cherd.2009.05.008. [6] A. Demirbas¸, Pyrolysis and steam gasification processes of black liquor, Energy Convers. Manage. 43 (2002) 877–884, http://dx.doi.org/10.1016/ S0196-8904(01)00087-5. [7] M. Cardoso, É.D. de Oliveira, M.L. Passos, Chemical composition and physical properties of black liquors and their effects on liquor recovery operation in Brazilian pulp mills, Fuel 88 (2009) 756–763, http://dx.doi.org/10.1016/j.fuel. 2008.10.016. [8] M. Cardoso, K.D. de Oliveira, G.A.A. Costa, M.L. Passos, Chemical process simulation for minimizing energy consumption in pulp mills, Appl. Energy 86 (2009) 45–51, http://dx.doi.org/10.1016/j.apenergy.2008.03.021. [9] M. Naqvi, E. Dahlquist, Bio-refinery system in a pulp mill for methanol production with comparison of pressurized black liquor gasification and dry gasification using direct causticization, Appl. Energy 90 (2012) 24–31, http:// dx.doi.org/10.1016/j.apenergy.2010.12.074. [10] T. Ekbom, M. Lindblom, N. Berglin, P. Ahlvik, Technical and Commercial Feasibility Methanol/DME Production as Motor Fuels for Automotive Uses—BLGMF, Nykomb Synergetics AB, Chemrec, Volvo, Ecotraffic, OKQ8, STFi, Progr. Rep. (2003) 1–298. Contract No. 4.1013/Z/01-087/2001. [11] T. Johnson, B. Johnson, K. Mukherjee, A. Hall, India—an emerging giant in the pulp and paper industry, 65th Appita Annu. Conf. Exhib. Rotorua New Zeal. 10-13 April 2011 Conf. Tech. Pap. (2011) 135. http://webcache. googleusercontent.com/search?q=cache:4wppefVmJTkJ:becaamec.co.nz/ media/∼/media/beca amec/media/technical papers/ india emerging giant pulp paper industry.ashx+&cd=5&hl=en&ct=clnk&gl=in. [12] Technology Compendium On Energy saving Opportunities Pulp & Paper Sector (2013). [13] O.P. Verma, G. Suryakant, G. Manik, Solution of SNLAE model of backward feed multiple effect evaporator system using genetic algorithm approach, Int. J. Syst. Assur. Eng. Manage. (2016) 1–16, http://dx.doi.org/10.1007/s13198016-0533-0. [14] J. Laurijssen, A. Faaij, E. Worrell, Energy conversion strategies in the European paper industry—a case study in three countries, Appl. Energy 98 (2012) 102–113, http://dx.doi.org/10.1016/j.apenergy.2012.03.001.

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