Preliminary Design of a Municipal Solid Waste (MSW) Biorefinery for

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Process Systems Engineering

Preliminary Design of a Municipal Solid Waste (MSW) Biorefinery for Environmental Friendly NH Production 3

Vitor Paixão, Argimiro Resende Secchi, and Príamo Albuquerque Melo Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b02927 • Publication Date (Web): 18 Oct 2018 Downloaded from http://pubs.acs.org on October 19, 2018

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Industrial & Engineering Chemistry Research

Preliminary Design of a Municipal Solid Waste (MSW) Biorenery for Environmental Friendly NH3 Production ∗

Vitor P. Paixão,

Argimiro R. Secchi, and Príamo A. Melo

Chemical Engineering Graduate Program, COPPE, Universidade Federal do Rio de Janeiro, UFRJ, Cidade Univesitária, Rio de Janeiro, RJ, Brazil, 21941-972 E-mail: [email protected]

Phone: +55 (0)19 971371734

Abstract A massive quantity of municipal solid waste (MSW) is generated yearly worldwide. The treatment and the nal disposal of this waste is both nancially and environmentally expensive. Also environmentally aggressive, the NH production process releases 3

great amounts of CO in the atmosphere. However, this process is essential for the 2

global economy. In this work, the economic performance of a plant, which combines MSW gasication with the ammonia production process, was studied. The results showed that the project has a low rate of return (0% - 4%). However, municipalities, which spend billions yearly on MSW treatment, could already benet from it. The gasier is the most expensive equipment in the process (approx. 55% of the total installed cost). Refrigeration and electricity are both a burden for the operating cost, accounting for almost 70% of it. Further reduction in the gasier price and an improved energetic integration could boost the economic performance.

Keywords: Waste treatment, Ammonia economy, Sustainability 1

ACS Paragon Plus Environment

Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Acronyms ACB AMR AST BF BL CND CP EMSO ER GA HE IRR ISR KV LCA MSW MX NPV PSA RTEA SP SRK ZB

Activated Carbon Bed Ammonia Reactor Ammonia Storage Tank Bag Filter Blower Condenser Compressor Environment for Modeling, Simulation and Optimization Equivalence Ratio Gasier Heat Exchanger Internal Rate of Return Isothermal Shift Reactor Knockout Vessel Leading Concept Ammonia Municipal Solid Waste Mixer Net Present Value Pressure Swing Adsorption Retro-Techno-Economic Analysis Splitter Soave-Redlich-Kwong ZnO Bed

2


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Notation ACB AMR C c F F F 2 GA m MSW NH P P P POP PSA r 3 S 3 T t T T T T T y y 3 y 3

a c

I

p

A,in

H ,out c

cat

p

3

A I

r

NH

NH

ads C g I

A,in

A,out

NH ,in NH ,r

∆H ν

Hydrogen sulde adsorption capacity in the ACB (kgH2 S /m3adsorbent ) Hydrogen conversion in the PSA unit (%) Installed cost (US$) Heat capacity at constant pressure (kJ/kmol/K) Molar ow rate (kmol/h) Total molar ow rate at the AMR inlet (kmol/h) Hydrogen molar ow rate at the AMR outlet (kmol/h) Gasier installed cost (US$) Catalyst mass (kg) MSW receiving price (US$/ton) Ammonia selling price (US$/ton) Pressure (atm) AMR operating pressure (atm) ISR operating pressure (atm) Population size (-) Hydrogen recovery in the PSA unit (%) Ammonia rate of formation (kmolNH3 /kgcat /h) Value received from ammonia sales (US$) Temperature (K) PSA unit adsorption time (s) Stream temperature at CND1 outlet (K) Gasier temperature (K) ISR operating temperature (K) Gas temperature at AMR inlet (K) Gas temperature at AMR outlet (K) Molar fraction (-) Ammonia molar fraction at AMR inlet (-) Ammonia molar fraction recovered in the gaseous stream in KV2 (%) Heat of reaction (kJ/mol) Stoichiometric coecient (-)

3



Introduction The NH3 production in industrial scale was made possible due to the work carried out by the scientists Fritz Haber and Carl Bosch. Their discovery is considered to be one of the most important of the last century because it made the production of fertilizers to increase which led to an increase in the food production and consequently, the world population itself 1 . On the other hand, the NH3 production consumes 1.2% of the primary global energy. Also, due to the reactions that occur in the production process, mainly during the steam reform of natural gas and light hydrocarbons, the NH3 production is responsible for 0.93% of all greenhouse gas emission worldwide 2 . The usage of renewable energy for NH3 production has been considered recently. Ref. 3 studied the possibility of producing NH3 in a pilot plant (approx. 26 ton/day of NH3 ) powered by wind energy. The required H2 is generated in a proton exchange membrane and N2 , in turn, in a pressure swing adsorption (PSA) unit. From the environmental point of view, this process conguration is very attractive because both the raw material and the required energy for its operation come from a renewable source and the greenhouse gas emission for this process is zero. However, there are some disadvantages. The plant is not economically competitive with larger plants based on natural gas steam reform. Also, the production of wind electricity is not continuous and the plant must be connected to a local power grid 4 . Similarly, Ref. 5 studied the possibility of producing ammonia from water electrolysis and air cryogenic separation. For a production capacity of 1,000 ton/day of NH3 , the electricity consumption was estimated to be 14% higher when compared to a usual natural gas steam reform Haber-Bosch process. However, the production cost showed that, even with a higher energy demand, the plant might be economically feasible. Expected future reduction in renewable energy cost, e.g., wind energy, and economic insensitive due to the virtually zero CO2 emission may transform this process conguration attractive. Ref. 6 developed a rigorous model to generate ammonia from water electrolysis and air cryogenic distillation. The developed model considers either wind or solar energy. The decision of 4


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which type of renewable energy to use is delegated to the plant model optimization. For a plant capacity of 300 ton/day powered by solar energy, the selling price estimated was approx. 4 times higher than the market price and, according to the authors, considerable eort still has to be made in order to make the proposed process conguration competitive. In an attempt to produce NH3 using a non-fossil fuel source, MSW appears to be a promising raw material. In 2012, the MSW generation in the world was estimated to be 1.3 billion tons and this value was predicted to double until 2025 7 . The decomposition of this residue directly aects the environment. It is estimated that 20% of CH4 originating from a greenhouse gas emission source comes from landlls 8 . The economic aspect is also a problem. In Brazil, for example, the costs due to MSW disposal is US$ 8.80 per ton if the landll is managed by municipalities and can achieve US$ 35.10 per ton if the landll is managed by private companies. Taking into account that in Brazil the MSW generation per capita is estimated to be 1.15 kg per day and the approximate population is 208 million people, one can roughly estimate that the Brazilian government spends billions on MSW disposal yearly 9 . The production of synthesis gas (syngas) from MSW gasication has been studied in the past years and the results are promising. In order to produce a syngas rich in H2 , steam has been found to be the best gasifying agent 10,11 . Air and O2 can generate a syngas with a similar quality although when air is used, the resulting syngas is diluted in N2 12,13 . The addition of CO2 in gasication with air decreases the H2 generation but keeps the syngas composition less susceptible to the raw material composition 7 . Regarding the gasication temperature, there is not a xed optimum value. It ranges usually from 700 K to 1200 K 7,1013 . The equivalence ratio (ER), i.e., the ratio between the amount of O2 supplied to the system and the amount of O2 required for complete combustion, was found to be ranging from 0.2 to 0.4 10 . The use of syngas produced during MSW gasication is already reported in the literature. The generated syngas is used to produce electricity and this scenario was economically 5



feasible for several population sizes with an internal rate of return (IRR ) higher than 10% 9 . Recycling and generation of biogas for electricity from MSW decomposition are also found to be capable of reducing environmental impact, save energy and generate income 8 . However, no study was found in the literature regarding the production of NH3 based on MSW gasication, although biomass gasication has already raised the interest of other scientists either to produce NH3 or other chemicals. Ref. 14 studied the possibility of producing H2 from the steam gasication of wood. Ref. 15 carried out an economic study of liquid fuel production based on the gasication of corn rusk. Ref. 2 and Ref. 16 studied the production of NH3 based on wood gasication. From these studies it was found that no matter what is being produced, the IRR and the biomass price are always between the variables that aect the plant feasibility the most. For NH3 production, the higher the plant capacity, the lower the NH3 minimum selling price. However, the NH3 minimum selling price for plants based on gasication was at least twice the NH3 actual market price at the time the present investigation was done. In the present study, we suggest an alternative for producing ammonia from hydrogen generated during the MSW gasication. The combination of MSW gasication with ammonia production is an attempt to both treat the MSW appropriately and to produce ammonia from a non-fossil source. In addition, a comprehensive mathematical model for the plant is developed in EMSO and can be further used as a starting point for future works. The economic feasibility of such a plant is evaluated by means of a retro-techno-economic analysis (RTEA) 17 .

Methodology General Assumptions 1. The plant is designed to receive MSW generated by 100,000 people;

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2. The MSW composition given by Ref. 18 is valid; 3. The main features of the Leading Concept Ammonia (LCA) are applied to design the plant 2 . In addition, information regarding the natural gas steam reform for NH3 production and the syngas purication are used 1922 ; 4. The received MSW is assumed to have a moisture content of 30%. Before it can be gasied, it passes through a manual separation and a pretreatment stage proposed by Ref. 9, where recyclables are also separated to be sold as by-products. In addition, 20% of the MSW is considered to be lost during the separation stage. This amount is burned to generated heat for the plant. The heat recovery for this stage is considered to be 70%; 5. The gasifying agent capable of producing a syngas with the highest content of H2 is steam 10,11 . However, the plant proposed in this study is meant to be installed next to cities and in the past few years, Brazil has suered from severe drought. Therefore, to minimize water consumption and, since N2 is required to produce NH3 , air is selected to be the gasifying agent; 6. The plant operates at steady state; 7. The Soave-Redlich-Kwong (SRK) equation of state is used to predict the thermodynamic properties; 8. Pressure drop is assumed to be 3% of the inlet pressure for each equipment.

Process description The treated MSW enters the gasier (GA) through stream S1 (Figure 1). The blower (BL) injects air into the gasier through stream S4. In this process conguration, the gasier replaces the reformers used for natural gas steam reform in the usual ammonia production process. This replacement was also made by Ref. 16, which used O2 to gasify wood and bark. 7



Usually, gasication processes with air or O2 generate a syngas with irrelevant concentration of CH4 12,16 . Dierently, Ref. 2 used a reformer after the gasier to convert the approximately 8% molar fraction of CH4 into CO2 and H2 . The CH4 concentration was obtained after simulating the gasication of wood using steam as gasifying agent. After gasication, the ashes are sent to a landll (S6). The generated gases are cooled in the rst heat exchanger (HE1) and then mixed (MX1) with a small amount of air previously split (SP1) from S3. This air stream is used only to provide O2 for enhanced H2 S removal in the active coal bed (ACB). Any particulate coming from the gasier is removed in the bag lters (BF). The gaseous stream is compressed (CP1) and the H2 S is further reduced in the ZnO bed (ZB). After the rst purication stage, the gases are mixed with steam (S14) coming from the boiler and the CO content is reduced in the isothermal CO shift reactor (ISR). The remaining steam is condensed (HE1 and CND1) and removed in the knockout vessel (KV1). The water recovered in S19 is used as coolant in HE1, ISR and HE4 and then vaporized in the boiler and reinjected in the plant through stream S14. The syngas nal purication is accomplished in the pressure swing adsorption (PSA) unit. Ref. 2 implemented a methanator after the PSA unit to remove CO. In this work, the PSA unit is designed to remove CO2 and N2 but the adsorbents (zeolite 5A and activated carbon) remove CO simultaneously. Since the ISR drastically reduces the CO concentration, the PSA unit can handle the remaining amount. Although Ref. 2 used the same concept to design the PSA unit, in their process the amount of N2 to be removed is smaller since steam is used as gasifying agent. Therefore, the zeolite 5A layer in their PSA unit is probably smaller and it is not able to complete the CO removal. Ref. 16 used an acid gas removal unit followed by a liquid N2 wash to purify the syngas after the shift reactor. Their choice for a dierent nal purication step arises from the diversity of existing syngas purication processes and, since their process was designed for large scale production capacity, the LCA was not used. After nal purication, the gases enter the Haber-Bosch loop where they are rstly compressed (CP2) and heated (HE3). NH3 is formed in the ammonia reactor (AMR). The heat 8


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S5 GA

HE1

S1

ACB

S7

S11

S12

S13 MX2 S15

S6 PSA

S22 S8

S4

ISR

CP1 S10

S9

S14

ZB

BF

S20

KV1

S3

S21

MX3

HE2

CND1

MX1 S18

S16

S17

SP1 S2

CP2 BL

HE3 S26

S23

S25 MX4 S35

AMR

S19

S33

S24

S32

S34

SP2

S28

KV2 AST

CP3

HE4

S29

S27

S30

CND2

S31

Figure 1: Process ow diagram of the ammonia production plant from MSW. generated during this reaction is partially recovered in HE4 and used to heat the gases coming from MX4. The produced NH3 is condensed (CND2), separated in KV2 and sent to the ammonia storage tank (AST). The unreacted gases are split (SP2) and mostly is recycled to the Haber-Bosch circuit (S34); the rest is sent to the PSA unit (S33) to avoid CH4 build up, which is inert in the Haber-Bosch loop. The owsheet presented in Fig. 1 along with the model equations for each equipment were implemented in the software EMSO 23 where they were solved and optimized. Both the relative and absolute accuracies were set equal to 10-6 in all simulations.

Gasier model In order to predict the syngas composition in the gasier outlet, the model proposed by Ref. 18 was used. The model is based on thermodynamic equilibrium and, although it cannot predict the formation of char and tar, for example, it can predict with satisfactory accuracy the generation of H2 and CO, which are the most important compounds in the syngas for the NH3 production process. To adapt the model to this work, further assumptions were made as follows: 1. The ashes inuence on the system was taken into account in the energy balance. The 9



thermodynamic properties were taken from its main constituent, i.e., SiO2 24 ; 2. The MSW enthalpy of formation was calculated according to the methodology proposed by Ref. 24; 3. All MSW sulfur content was converted to H2 S; 4. The air moisture content was taken into account. Its relative humidity was considered to be 60%.

Equipment sizing Heat exchangers The heat exchangers present in the plant are considered to be shell and tubes with oating head. The overall heat exchange coecients were estimated from Ref. 25 and, for those inside the Haber-Bosch circuit, from Ref. 26.

Compressors and drivers The centrifugal compressor power was calculated using the correlation given by Ref. 27. In addition, whenever the compression ratio overcome 4, another compression stage is added. When 2 or more compressor stages are used, heat exchangers perform interstage cooling. The temperature after the interstage cooling was set equal to the entrance temperature of the rst stage. The polytropic eciency was assumed to be 78% and the compression ratio is equal for all stages 26,27 .

Active coal bed and ZnO bed Active coal bed and ZnO bed are designed to remove H2 S from the syngas. The active coal bed was considered to operate for 720 h before the adsorbent must be replaced. To avoid downtime, two beds with equal size were considered so that when one operates the

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other has its adsorbent replaced. The active coal adsorption capacity is considered to be 300 kg of H2 S/m3 of coal 28 . The active coal bed was assumed to reduce the concentration of H2 S to 3 ppm 29 . With the operating time, the adsorption capacity and the amount of H2 S generated in the gasier, one can easily calculate the active carbon bed volume. The ZnO bed (ZB) was sized similarly to the active coal bed. However, since the H2 S concentration is drastically reduced in the active coal bed, the ZnO bed operates for 31.680 h. It reduces the H2 S concentration further to 0.0099 ppm 19 . With the aid of Equation 1 and the amount of H2 S that must be removed, the required ZnO mass was obtained. Assuming a bed porosity of 50% and ZnO density of 5.606 kg/m3 , the bed volume was calculated 30 .

H2 S + ZnO ←→ ZnS + H2 O

(1)

ISR The ISR is used to reduce the concentration of CO, a poison for the NH3 catalyst. This reactor is considered to be a shell and tube with oating head heat exchanger whose tubes are lled with a CuO-ZnO/Al2 O3 catalyst 19 . The heat removed was calculated using the heat of reaction given by Ref. 21. The ISR was assumed to be able to reduce the CO concentration to 0.002 mol/mol in dry basis 19 . The required catalyst mass was calculated according to Ref. 27.

PSA unit The PSA unit is responsible for the syngas nal purication. The main advantages of using the PSA unit are, according to Ref. 31, the achievement of high purity levels (99.99%), continuous operation and product recovery of up to 90%. PSA units ranging from 4 to 12 beds are common in the industry. With the increase of the number of beds, the product purity and recovery increase as well as the unit capital cost 32,33 . In this work, a PSA unit with 6 beds was considered. Each bed consists in a rst 11



layer of silica gel, which removes residual water vapor, a second layer of activated carbon, which removes mainly CO2 and a third layer of zeolite 5A. Since gasication is performed with air, the nitrogen content is higher than the requested to generate ammonia. Zeolite 5A adsorbs nitrogen with a certain level of selectivity when compared to the other compounds present in the gas mixture. Therefore, dierently from the other two previous layers, which were sized to completely remove water and CO2 , the zeolite 5A layer is sized to adsorbs N2 until the ratio H2 /N2 achieves 3 2 . The H2 recovery was assumed to be 75%. The eective use of the bed varies from 70%, for a PSA unit with 2 beds, and can achieve 100% for PSA units with several beds if only one component is signicantly adsorbed 32 . In this work, 6 beds were considered but the adsorption is multicomponent. Therefore, an eective use of the bed of 75% is assumed. The PSA unit is sized according to Ref. 34. The equilibrium data for multicomponent adsorption in activated carbon is given by Ref. 35 and, in zeolite 5A, by Ref. 36. The silica gel layer is considered to selectively adsorbs water. For this substance, the equilibrium data is given by Ref. 37. The PSA o-gas is the mixture of gases removed during the purication step in the PSA unit. Since the adsorption is not 100% selective, some H2 is also removed along with CO, CO2 , N2 , H2 O and CH4 . The PSA o-gas (stream S21 in Figure 1) has a low caloric value which is used to produce either steam or electricity or both, depending on the energetic arrangement. To generate electricity, the system proposed by Ref. 9 was used. This system overall eciency was assumed to be 33%.

AMR design The NH3 formation (Equation 2) occurs according to an equilibrium limited exothermic reaction. To form NH3 in a signicant amount, the presence of a catalyst, usually iron based, is required 19 .

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N2 + 3H2 ↔ 2N H3

(2)

The AMR was assumed to have one single catalytic bed. It is well-known that commercial ammonia plants with capacities greater than 1,000 ton/day are usually equipped with multiple bed ammonia converters 22 . However, small capacity plant, which is the case of the present work, suers from the elevated capital cost. Therefore, in order to mitigate the eect of the converter cost on the plant capital cost, a reactor with one single bed was selected. In order to estimate the reactor s cost and its size, the catalyst mass is needed. Since a simple equilibrium model cannot estimate this variable, a mass balance coupled with chemical kinetics approach was adopted in this work. To calculate the required catalyst mass (mcat ), Equation 3 was used 27 . To account for temperature (T ) variations inside the reactor, Equation 4 was used 27 . ν is stoichiometric coecient of the substance i, F is the molar ow rate (kmol/h) and cp is the uid heat capacity at constant pressure (kJ/kmol/K). The rate of reaction (rNH3 )(kmolNH3 /kgcat /h), in which the parameters were estimated based on experimental data performed in conditions encountered in commercial reactors, is reported by Ref. 27. The heat of reaction (∆H ) is given by Ref. 38.

dFi = νi rN H3 dmcat

(3)

dT rN H3 (−∆H) = dmcat F cp

(4)

Equation 3 and Equation 4 cannot be directly solved because both mcat and the NH3 nal concentration are unknown. To overcome this problem, the following procedure was proposed: 1. Use backward Euler's method to transform the continuous Equation 3 and Equation 4 into discrete ones using a step of 0.05 (kg of catalyst); 2. On every integration step, check the dierence between FNH3 of the actual step and of 13



the previous step. If this dierence is lower than 0.00001 and the NH3 molar fraction (yNH3 ) is higher than 0.07, then the calculation is interrupted and the catalyst mass is evaluated at the stopping point. Step 2 is justied because it is considered that if the requirements are met, the reaction achieved equilibrium and the use of more catalyst will not aect it. The condition yNH3 > 0.07 is used to avoid stopping the calculation prematurely. On the early calculation steps, the dierence between FNH3 of the actual step and of the previous step might be lower than 0.00001. It is known that yNH3 at the AMR inlet is lower than 0.05 and, at the outlet, for one single bed, not higher than 0.15 19 . Therefore, 0.07 is not an equilibrium concentration and was considered appropriate. The aforementioned procedure cannot be directly implemented in EMSO because it solves all equations simultaneously, i.e., it does not iterate. Therefore, the procedure was executed in MATLAB R2008 where the model was simulated for dierent conditions (Table 1). At the end of every simulation, the nal result was saved. To accomplish the plant optimization and the RTEA neither the behavior of the temperature nor the behavior of the components molar ow along with the catalyst mass increase are required. Only their values at the AMR outlet are necessary. Therefore, three metamodels were proposed to describe the needed AMR design variables: one for mcat , one for the AMR outlet temperature (TA,out ) and one for the H2 molar ow rate at the AMR outlet (FH2,out ). These 3 variables are directly calculated as functions of the AMR inlet temperature (TA,in ), AMR inlet NH3 molar fraction (yNH3 ,in ), AMR inlet total molar ow rate (FA,in ) and AMR operating pressure (PA ). The proposed model is represented in Equation 5, where aijk are parameters to be estimated using the data previously obtained from the simulations with MATLAB R2008. Variable represents either mcat or TA,out or FH2,out . In essence, Equation 3 and Equation 4 are replaced by the metamodel (Equation 5) so that the simulations in EMSO can be performed.

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V ariable = a0 + a1 FA,in +

3 X

ai00 PAi

+

i=1

+

2 X

k a00k yN H3 ,in +

3 X 2 X 2 X

2 X

j a0j0 TA,in

j=1

(5)

j k aijk PAi TA,in yN H3 ,in FA,in

i=1 j=1 k=1

k=1

In Table 1, the range of Fin was chosen according to a previous plant simulation before the AMR model implementation. From this simulation, it was noticed that the fresh feed sent to the Haber-Bosch circuit (stream S24) was equal to 125 kmol/h. The recycling stream (S35) is usually 4 to 5 times higher than the fresh feed 19 . Therefore, an amplied range was selected for FA,in . The PA interval was chosen based on what is found on literature 2,3 and according to the LCA. The temperature range was selected by taking into account that temperatures below 540 K would make the reaction kinetics slow for the pressure range selected and, for temperature above 620 K, reaction equilibrium would be achieved rapidly resulting in small conversions. The yNH3 ,in is usually below 0.05 19 and for values below 0.01 a low temperature is required in CND2, which increases the operating costs. Table 1: Range of the variables used to simulate the discrete model.

F

(kmol/h) 250 - 900 with increment of 50 A,in

P (atm) A

60 - 150 with increment of 5

T

(K) 540 - 620 with increment of 5 A,in

y

(%) 1 - 5 with increment of 0.5 NH3 ,in

The parameters aijk (Equation 5) were estimated with the aid of the software Statistica. The Levenberg-Marquardt algorithm with a condence level of 95% was applied. The objective function was the well-known least squares and the absolute tolerance used was 10-6 . During the estimation, the parameters considered not signicant to the model were removed. A parameter was considered not signicant to the model when the value zero belongs to its condence interval. Student's t-distribution was used to calculate the parameters condence interval. This procedure was repeated until the determination coecient between the simulated data (in MATLAB R2008) and the data predicted by the empirical model (Equation 5) 15



was higher than 0.999. The obtained empirical models were implemented in EMSO and the owsheet was simulated to calculate mcat . With mcat , the AMR catalyst density (4,900 kg/m3 ) and the bed porosity (0.46), the AMR volume was obtained 27 .

Miscellaneous equipment Pumps and the AST were designed according to Ref. 26. For each installed pump, another one was installed as spare. The AST was designed to store the NH3 generated during 20 days of production. The KV was designed according to Ref. 32. The BL was designed according to Ref. 39. Its eciency was assumed to be 75% and the discharge pressure was set to 1.15 atm.

Energetic arrangement and compressor drivers A Rankine cycle is selected to produce steam and power. In order to choose the most adequate compressor driver, i.e., steam turbine, internal combustion engine and electric motor, one must know the type of energy that can be generated using subproducts inside the plant. Therefore, three dierent energetic arrangements were proposed in this work, as follows: 1. Steam turbines: Vapor is generated by burning the MSW lost during the separation stage. If the produced heat is not enough, the PSA o-gas is also burned. For this case, if there is an excess of PSA o-gas, this excess is used to produce electricity as stated in the PSA unit subsection. If the MSW and PSA o-gas combustions does not fulll the heat demand, natural gas is burned. Steam is generated in the boiler and sent to the steam turbines to produce power. The steam discharged by the steam turbines is split and a fraction goes to the ISR and the remaining is condensed and pumped back into the boiler. Makeup water is required due to the water consumption in the ISR.

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Water used as coolant in HE1, ISR and HE4 and the recovered portion from stream S19 is sent to the boiler; 2. Internal combustion engines: The PSA o-gas is burned in internal combustion engines to drive the compressors. If PSA o-gas exceeds the demand, this excess is used to produce electricity. If PSA o-gas is not enough, natural gas is used. The steam produced is used directly in the ISR. Therefore, only the boiler is required in this scenario; 3. Electric motors: The PSA o-gas is used to produce electricity to drive the compressors. If electricity is produced in excess, then it is sold. If it does not fulll the demand, electricity is purchased from the local power grid. Steam is produced as stated in 2. In all scenarios discussed above, 30% of the generated or recovered heat was considered to be lost. Each scenario generates a partially dierent owsheet. The best scenario is chosen after optimization.

Flowsheet optimization The owsheet optimization was accomplished using both complex 40 and the direct 41 algorithms. EMSO has, among others, these two derivative-free algorithms available on its optimization environment. The selected decision variables were the gasication temperature (Tg ); the ISR operating temperature (TI ) and pressure (PI ); the CND1 temperature (TC ); the PSA adsorption time (tads ); the AMR inlet temperature (TA,in ) and operating pressure (PA ); the NH3 molar fraction recovered in the gaseous stream in KV2 (yNH3 ,r ). The objective function was dened as the sum between the net present value (NPV) and the value received from the NH3 sales (SNH3 ). The SNH3 was inserted in the objective function to always keep the NH3 production on its highest value minimizing the preference for electricity production. The NPV and SNH3 are normalized in the same scale of magnitude. 17



The optimization problem is stated as follows: maximize

(N P V + SN H3 )

subject to: Plant's Mathematical Model

700 K ≤ Tg ≤ 1200 K Tvapor ≥ (−0, 0259PI2 + 3, 5244PI + 450, 04) K TI ≤ 536 K 2, 5 atm ≤ PI ≤ 30 atm TC ≤ 315 K 50 s ≤ tads ≤ 600 s 60 atm ≤ PA ≤ 150 atm 540 K ≤ TA,in ≤ 620 K 0, 01 ≤ yN H3 ,r ≤ 0, 05 The variables boundaries selection is explained in Table S2 (Supporting Information). The variable Tvapor is not a decision variable but only a constraint used to avoid steam condensation in S14. A multistart optimization using 10 dierent initial guesses randomly selected (Table S3) was carried out using the complex algorithm. This was done due to the existence of local minima, to reduce the decisions variable search domain in order to better apply the direct algorithm and to facilitate the solver's convergence. After implementing each equipment in the owsheet when programing in EMSO, it is recommended to save the simulation results and use them as initial guess for further simulations in order to achieve owsheet convergence. EMSO uses Newton-Raphson algorithm to solve the algebraic equations and it might be sensible to the initial guess when solving the model equations. If the direct algorithm is applied without reducing the domain of the decisions variables, at some point during the optimization, the decision variables values would be far from the initial guess. This would 18


Page 18 of 60

Page 19 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


make the solver to diverge. Therefore, after carrying out a multistart optimization with the

complex algorithm, the best similar results obtained were used to generate a reduced domain. Then, the direct algorithm was applied for global optimality in the reduced domain 41 .

Economic study The equipment installed cost (CI ) are estimated according to the data provided by Ref. 39, Ref. 42 and Ref. 32. The CI for the MSW separation and pretreatment sections is given by Ref. 9. Ination is taken into account using the average 2016 Chemical Engineering Plant Cost Index 43 . The gasier considered in this work is a direct red circulating uidized bed (CFB). Its price is estimated according to the correlations given by Ref. 20. Ref. 44 provides an extensive study about gasication technologies available. CFB gasiers are already available in the market at dierent capacities, they are capable of operating with several types of residues, including MSW, and their cost is competitive with other gasier types available. 44 The labor cost is calculated according to Ref. 9 using the 2016 average minimum wage in Brazil of US$ 3,231. The raw material cost is considered to be negative because municipalities pay between US$ 17.80 and US$ 35.10 per ton to private companies for this service 9 . In this work, a value of US$ 10 per ton of received MSW was considered because MSW collection and transportation costs are not included in the current evaluation. Utilities cost are calculated according to Ref. 45 by considering natural gas to be the fuel source with a price of US$ 12.26 per GJ 46 . The electricity price is US$ 144.83 per MWh 47 if purchased from the power grid and considered to be US$ 100 if sold as subproduct. The NH3 selling price is considered to be US$ 500 per ton. This is the average price from the last decade 48 . Glass, metal and plastic are sold to recycling companies at the prices of US$ 0,023 per kg, US$ 0,04 per kg and US$ 0,086 per kg, respectively 49 . To evaluate the plant economic feasibility, the net present value (NPV) was used. The project life is 20 years considering 7 years for total depreciation and an IRR of 15%. 19



Page 20 of 60

Results and Discussion Gasier model To evaluate the model prediction capacity, the simulation results of wood gasication were compared with the experimental data given by Ref. 50. The model results and prediction error is depicted in Table 2. The error was calculated subtracting the experimental result from the simulated one and dividing the resulting value by the respective experimental result. As can be seen, when the air/fuel ratio is specied and the gasier temperature (Tg ) is calculated, the values predicted by the model (second column of Table 2) dier signicantly from the experimental results, mainly the H2 and N2 molar fractions and Tg (third column of Table 2). This molar fraction discrepancy is due to the dierence between experimental and predicted Tg . To sustain this hypothesis, another simulation was performed by specifying Tg and predicting the air fuel ratio (fourth column of Table 2). With this procedure, the molar fractions predicted are very close to that obtained experimentally, with exception to the CH4 molar fraction, which is very close to zero (fth column of Table 2). This is expected since the temperature is high and there is O2 in the system. Table 2: Model prediction error in comparison with the experimental data reported by Ref. 50. Molar fraction (dry basis)

Model results (Tg predicted)

H2 CO CO2 CH4 N2 Air/fuel ratio Gasication temperature (Tg )

0.217 0.185 0.125 0.007 0.466 specied

Model prediction error (Tg predicted) (%) -40.00 3.14 -9.65 36.36 11.91 specied

955.45 K

20.38

20


0.159 0.181 0.113 0.00036 0.546 2.34

Model prediction error (Tg specied) (%) -2.58 5.24 0.88 96.73 -3.21 -25.81

specied

specied

Model results (Tg specied)

Page 21 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


One point of interest is determining the reason why the predicted Tg diers signicantly from the experimental Tg when the air/fuel ratio is specied. This might be related to the methodology used to estimate the biomass enthalpy of formation. The exact value of the biomass enthalpy of formation is very hard to be obtained and, since the implemented model depends strongly upon the thermodynamic data, any discrepancy may cause an error in the prediction composition. Another reason for the dierence between the simulated results and the experimental results might be caused by the approximation of the ashes thermodynamic properties by the SiO2 properties. To achieve the experimental Tg , the predicted air/fuel ratio increases considerably approximating the gasication conditions towards combustion. This indicates that the system, although assumed adiabatic, may need more energy to increase its temperature. The only inert material capable of removing heat from the system are the ashes, i.e., the SiO2 . Because the model is only based on thermodynamic data, and since the biomass ash content is small, the temperature discrepancy is attributed mainly to the accuracy of the methodology used to obtain the biomass enthalpy of formation. The model is considered able to predict the syngas composition when Tg is specied. Although the required air/fuel ratio is considerably higher, this could only lead to a worst case scenario since more energy is required to inject air into the system and the syngas becomes more diluted in N2 . The gasier model was used to predict the syngas generated during MSW gasication as a function of Tg . The MSW mass ow at the gasier inlet is 2,457.4 kg/h with a moisture content of 6.52%. This mass ow was obtained after the MSW pretreatment and separation stages. The results are depicted in Figure 2. As Tg increases, the equivalence ratio (ER) also increases, leading to the dilution of the gaseous stream by N2 (Figure 2-c). The consequence of this can be seen comparing each component molar ow rate (Figure 2-a) with its molar fraction (Figure 2-b).

21


a

Page 22 of 60

80

H2

60

CO CO2

40

H2O

20

CH4

0 650

H2S

700

750

800

850

900

950

1000 1050 1100 1150

Molar fraction

Gasifier temperature (K) b 0.3

H2

0.2

CO CO2 H2O

0.1 0 650

CH4 H2S

700

750

800

850

900

950

1000 1050 1100 1150

Gasifier temperature (K) c 0.6

ER

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Molar flow (kmol/h)


0.4 0.2 650

700

750

800

850

900

950

1000 1050 1100 1150

Gasifier temperature (K)

Figure 2: Synthesis gas generation in the gasier. a - Component molar ow rate versus gasier temperature. b - Component molar fraction versus gasier temperature. c - Equivalence ratio versus gasier temperature. The N2 content is taken into account for molar fraction calculation. The CH4 formation decays as Tg increases until nothing is formed. The H2 and the CO formation increases until a maximum is achieved at 900 K and 1000 K, respectively. This behavior is qualitatively similar to the experiments carried out by Ref. 12. From 1000 K upwards, the formation of CO and CO2 remains constant (Figure 2-a) while the H2 formation decreases. Since CH4 is almost not formed, Tg rises at expense of H2 by forming H2 O instead.

AMR model In order to prove the eectiveness of the integration interruption criteria proposed in the AMR design subsection, the AMR discrete model was simulated in MATLAB R2008 for 22


Page 23 of 60

dierent initial conditions. The results of two dierent simulation are presented in Figure 3 and Figure 4. The calculated catalyst mass in Figure 3 was 12,725 kg and, in Figure 4, was 11,565 kg. From these results one may see that once concentrations and temperature inside AMR achieve equilibrium, the integration is promptly interrupted. Therefore, this model was used to generate data to estimate the parameters of the metamodel (Equation 5), resulting in the Equation 6, Equation 7 and Equation 8. b

0.12

750

Temperature (K)

NH3 molar fraction

a 0.1 0.08 0.06 0.04 620 645 670 695 720

700 650 600

Temperature (K)

0

4000 8000 12000

Catalyst mass (kg) c

0.8

Molar fraction

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.6

H2 N2

0.4

NH3

0.2 0

0

4000

8000

12000

Catalyst mass (kg)

Figure 3: Discrete AMR model simulation. yNH3 ,in =0.05, TA,in =620 K e PA =70 atm.

23

Initial condition:


F

A,in

=400 kmol/h,


b

0.15

800

Temperature (K)

NH3 molar fraction

a

0.1 0.05 0 580

640

700

750 700 650 600 550

760

Temperature (K)

0

4000 8000 12000


0.8

Molar fraction

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 60

0.6

H2 N2

0.4

NH 3

0.2 0

0

4000

8000

12000

Catalyst mass (kg)

Figure 4: Discrete AMR model simulation. yNH3 ,in =0.03, TA,in =580 K e PA =150 atm.

Initial condition:

F

A,in

=700 kmol/h,

FH2 = 0.33439FA,in + 5.4364 × 10−4 FA,in TA,in − 8.4109 × 10−4 FA,in PA FA,in yN H3 ,in 2 TA,in −7 − 1.068 × 10 yN H3 ,in

+2.0758 × 10−6 FA,in PA2 + 3.0527 × 10−4 +5.491 × 10−7

PA2 yN H3 ,in

(6)

TA,out = 4.807031 × 102 + 1.715229 × 10−5 PA3 − 7.91641 × 10−3 PA2 2 +1.663656PA + 1.3944 × 10−4 TA,in + 0.220815TA,in

−6.1616 × 102 yN H3 ,in +

24


1.7925 × 10−2 yN H3 ,in

(7)

Page 25 of 60

FA,in yN H3 ,in FA,in yN H3 ,in + 2.251457 × 1012 2 2 2 PA TA,in PA TA,in FA,in yN H3 ,in FA,in yN H3 ,in + 5.8072 × 106 −7.35189 × 109 PA TA,in PA FA,in FA,in +4.9148 × 108 2 − 2.493 × 107 3 2 TA,in PA PA FA,in FA,in +8.3572 × 106 2 − 7.053 × 103 TA,in PA

mcat = 3.3943 × 1012

(8)

+1.63 × 10−6 FA,in PA3 TA,in yN H3 ,in − 7.16 × 10−4 FA,in PA2 TA,in yN H3 ,in 2 +6.75 × 10−2 FA,in PA TA,in yN H3 ,in + 1.7 × 10−7 FA,in PA2 TA,in yN H3 ,in

The values predicted by the empirical model compared to the values obtained simulating the discrete model were in good agreement, as can be seen in Figure 5, Figure 6 and Figure 7.

Predicted (kmol/h of H 2 )

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


600 500 400 300 200 100 100

Ideal Predicted

200

300

400

500

600

Simulated (kmol/h of H 2 )

Figure 5: FH2 predicted by empirical model versus FH2 simulated by discrete model. Determination coecient equals to 0.9999.

25



800

Predicted (K)

780 760 740 720 700 680 680

Ideal Predicted

700

720

740

760

780

800

Simulated (K)

Figure 6: TA,out predicted by empirical model versus TA,out simulated by discrete model. Determination coecient equals to 0.9998.

20

× 104

15

Predicted (kg)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

10

5 Ideal Predicted

0 0

5

10

15

Simulated (kg)

20 × 104

Figure 7: mcat predicted by empirical model versus mcat simulated by discrete model. Determination coecient equals to 0.9994.

Plant optimization The nal optimization results are depicted in Table 3. Plant 1, 2 and 3 refer to the owsheet where steam turbines, internal combustion engines and electric motors are used as compressor drivers, respectively. From Table 3 one can see that the decision variable values are the same for Plant 2 and Plant 3, but the objective functions are dierent. This

26


Page 26 of 60

Page 27 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


occurs because both plants are very similar and the dierence between them lies only on the compressor driver. The lower eciency of the internal combustion engine (Plant 2) is counterbalanced by the lower cost of natural gas when compared to the electricity price (Plant 3 with electric motors). Plant 1 results are very dierent because it has a Rankine cycle, which is used to generate steam for the turbines and for the ISR. Plants 2 and 3 have only the boiler generating steam at the required T and P for the ISR. Table 3: Optimization nal result using complex followed by direct algorithms. Variable Tg (K) TC (K) TI (K) PI (atm) PA (atm) TA,in (K) yNH3 ,r (%) tads (s) NPV (US$×10−7 ) SNH3 (US$×10−6 ) Objective function

Plant 1 924 256.73 463.47 3.32 60.19 540.47 5 50 -4.78505 3.49807 -1.28698

Plant 2 924 274.05 517.24 2.5 94.5 570.4 4.41 50 -3.87576 3.5000 -0.37576

Plant 3 924 274.05 517.24 2.5 94.5 570.4 4.41 50 -3.9161 3.5000 -0.4161

Plant 1 presented the worst performance. This occurs because the MSW and PSA ogas combustion was not enough to produce the steam required by the turbines. Therefore, natural gas was purchased which resulted in an annual cost of US$ 1,410,216. For plant 2, this value was US$ 119,725 because, although the MSW and PSA o-gas combustion supplied the heat required to produce steam, natural gas was burned with part of the PSA o-gas to generate power for the compressors. For Plant 3 this value was null but the annual cost with electricity for the electric motors was US$ 626,129. The variable tads for all plants was the same. This occurred because the higher the tads the higher the PSA unit capital cost. Therefore, the minimum cost for the PSA unit was found when tads is on its lower bound. The variable Tg for all plants was also equal. This eect is related with SNH3 in the objective function. When Tg achieves 924 K, the sum between the produced amount of H2 and CO achieve its maximum (Figure 2) which leads 27



to the maximum production of NH3 . The PI usual value for plants based on natural gas steam reform is between 10 atm and 30 atm 19 . In this work, the PI is far from this range. This occurs because the reformer, which is positioned before the ISR, operates at such pressure so that the compression work can be reduced in plants based on natural gas steam reform. The reactions occurring in the reformer increase the total number of moles and this leads to a higher power requirement in the subsequent compressors 22 . In the plants proposed in this work, there is not a steam reformer. Therefore, the pressure decreases to its minimum to minimize the compression work. In addition, the high N2 content resulting from the air gasication favors lower operating pressures. Since PI decreases, in order to keep the reaction in the ISR kinetically advantageous, TI is higher than the usual value of 473 K 19 . This eect was not observed in Plant 1 because for a higher TI , a higher steam ow rate in the turbines would be required and consequently more natural gas must be purchased, thus jeopardizing the plant economic performance as discussed before. The low value of PI led also to a reduction of TC to enhance the adsorption in the PSA unit despite its low operating pressure. For small capacity NH3 production plants to be economically competitive, the pressure inside the Haber-Bosch circuit should be lower than the usual range between 150 atm and 300 atm 2,22 . This is one of the features of the LCA 2 , which is reinforced in this work due to the lower value of PA . Regarding TA,in , its minimum value for the pressure range between 150 atm and 300 atm is 620 K 22 . Since the reduction of PA shifted the equilibrium of the reaction (Equation 2) towards the reagents, to keep the NH3 formation at favorable levels,

T

A,in

also decreased. The reduction in TA,in decreases the reaction kinetics, which makes

the required catalyst mass increase. However, there is a balance between the increase in catalyst consumption and the required compression work. According to the optimization, this balance is found at 94.5 atm and 570.4 K. The dierent results obtained for Plant 1 are again attributed to the reduction in natural gas consumption for a lower PA . Plant 2 was selected for further analysis because it showed the best economic result 28


Page 28 of 60

Page 29 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


according to the objective function (Table 3). The main streams results for Plant 2 after optimization are available in the Supporting Information le (Table S4).

Economic performance The most representative equipment for the plant installed cost (Table 4) are the gasier, the compressors and the heat exchangers, as reported before (Ref. 2). The total capital investment was US$ 46,303,801 for a plant capacity of 21.21 ton of NH3 per day. Table 4: Selected equipment installed cost. Equipment Heat exchangers Compressors MSW pretreatment and separation Gasier ISR AMR

C (US$) I

1,051,591 2,641,755 879,538 8,746,980 208,734 411,824

% 6.56 16.48 5.48 54.57 1.30 2.57

The total annual cost of production was estimated to be US$ 4,239,070. The greatest contributor to this value was the utilities cost (Figure 8). In addition, refrigeration and electricity account together for more than 70% of the utilities cost, and the raw material cost was negative according to the assumption made in the economic study section. The total value received due to sales was US$ 3,813,140. Approximately 92% of this value resulted from NH3 sales and the rest was due to the recyclables sales. The NH3 minimum selling price for this plant to be feasible, i.e., NPV equals to zero, was calculated to be US$ 2,350 per ton.

29



20

Production Costs (US$)

×10 5

15

10

5

0

es tra ea in rc in h g pe a C n rv a d ta is de io ly st n ve an lo pm d m Se en an llin t ag g em an en d m t ar ke R tin aw g m at er ia l

an ce ur

d R

Su

an s

fit

Be

s xe

ne

an d

m ad Ta

al an d G

en er

in s

in is

tra

tiv

e

bo r La

an ce

en

nt

U

til iti es

-5

M ai

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 60

Figure 8: Annualized total cost of production components. A comparison between the minimum selling price found in other studies is presented in Table 5. It is possible to notice that with the plant capacity increase, the minimum selling price decreases. However, for all studies, the minimum selling price was still higher than the market average price from the last decade of approximately US$ 500 per ton 48 . Table 5: NH3 selling price comparison. Author Ref. 16 Ref. 2 This work

Capacity (ton of NH3 /day) 700 65 21.21

Raw material Wood Wood MSW

IRR

(%) 10 - 20 15 15

NH3 selling price (US$/ton) 772 - 1,173 1,153 2,350

Sensitivity analysis and RTEA The eect of some variables on the NPV was investigated by means of a sensitivity analysis. The most inuent ones were selected for the development of the RTEA. The sensitivity analysis was carried out considering a variation range of ±30% with a step of 1%. 30


Page 31 of 60

The results are shown in Figure 9. The NPV was normalized dividing its current value by its absolute value when all variables have 0% variation. The variables ACBa , PSAr , MSWp and AMRc were not considered in the RTEA as they have minor eect on the NPV. -0.8

-0.8

POP ACB a

-0.85

NPV normalized

-1 -1.05

-1 -1.05 -1.1

-1.15

-1.15 -10

0

10

c

20

-1.2 -30

30

GAc

-0.95

-1.1

-20

IRR AMR

-0.9

NH3p

-0.95

-1.2 -30

MSWp

-0.85

PSAr

-0.9 NPV normalized

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


-20

-10

%

0

10

20

30

%

Figure 9: Sensitivity analysis result. The inuence of the MSW composition on the SNH3 minimum was also studied and the results are depicted in Table 6. The SNH3 minimum is substantially sensitive to variations in the MSW composition which makes the process more complicated to be designed and implemented in the future. Since it is almost impossible to control the MSW composition, a gasier able to keep the syngas quality constant for dierent raw materials is required. The correct choice of a gasifying agent might also play an important role in this case. Ref. 7 showed that the addition of CO2 in the gasication medium could help keeping the syngas composition more constant for dierent raw materials. Regarding environmental releases, especially related to CO2 emission, one possible solution is to partially recover the CO2 with a high purity degree in the PSA unit and use it in the gasier. For this scenario, performing a rinse step during the PSA cycle execution as proposed by Ref. 34 may be sucient.

31



Page 32 of 60

Table 6: Inuence of the MSW composition on the SNH3 minimum. Reference

Molecular formula

a

SNH3 minimum (US$/ton)

Ref. 18

CH1,502 O0,5189 N0,0216 S0,0024 (SiO2 )0,0504

2,350

Ref. 51

CH1,3238 O0,4378 N0,004 S0,0026 (SiO2 )0,0218

2,020

CH1,558 O0,4768 N0,019 S0,001 (SiO2 )0,0055 Molecular formula normalized in C.

1,970

Ref. 52 a

The variables GAc , IRR, POP and NH3p were separated into 3 groups, each with 3 variables, so that the RTEA can be better visualized. The NH3p is present in all groups and was dened as the calculated variable. This was done because NH3p is the only calculated variable that guarantees the achievement of the feasibility criteria, i.e., NPV equals to zero. The results are depicted in Figure 10, Figure 11 and Figure 12. In Figure 10, the calculated NH3p suered the smallest variation compared to Figure 11 and Figure 12. Since for this case the plant total production capacity was kept constant, the IRR and the GAc have together a minor eect on the NH3p . However, when each of these two variables changes along with the plant production capacity, it is possible to see the enhancement of their eect on NH3p with the increase of POP (Figure 11 and Figure 12). Nevertheless, even reducing IRR to 10% or GAc to 50% its actual price and increasing the plant capacity 15 times, the obtained NH3p is approximately US$ 300 higher than the last decade average market price. This leads to a project economically infeasible for private investors.

32


Page 33 of 60

20 280

0

19

22

00

240

0

18

260 0

IRR (%)

17

200

220

0

16

240 0

0

15 200

14

220

0

0

13

NH3 p (US$/ton)

180

0

200

12

0

11 180 0

160 0

10 50

55

60

65

70

75

80

85

90

95

100

Ga c (%)

Figure 10: RTEA 1. Internal rate of return versus gasier installed cost. NH3 minimum selling price calculated so that NPV equals zero.

×10 5 14

NH3 (US$/ton) p

10 00

80

0

12 00

12

10

POP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


8 00 14

00

6

12

00 10

0

140

4

0 120 2 10

1400 11

12

13

1800 2200 14 15

1800 2200

16

17

18

19

20

IRR (%)

Figure 11: RTEA 2. Population size versus internal rate of return. NH3 minimum selling price calculated with NPV equals to zero.

33



×10 5 NH3 p (US$/ton)

14

12 0 90

0

80

10

00

10

POP (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 60

8

0

100

900

1200

6

1000 1200

4

1400

1400

2 50

1800 55 60

1800

65

70

75

80

85

90

95

100

GAc (%)

Figure 12: RTEA 3. Population size versus gasier installed cost. NH3 minimum selling price calculated with NPV equals to zero. The possibility of public expenditure reduction was also studied. This was accomplished considering an IRR ranging from 0% to 10%. Obviously, the higher the IRR, the better is the project. However, in Brazil, municipalities spend billions yearly with MSW treatment. Therefore, the lower range of IRR is not a bad scenario if it results in a reduction of the public expenditure. For this part of the study, the MSW receiving price, i.e., the raw material price, was set to US$ 0 since the project is meant to be developed and operated by municipalities. Considering the current scenario, where all other variables were kept constant except IRR and POP (Figure 13), it is possible to see that a competitive price, i.e., bellow US$ 500 per ton, can be obtained for cities with populations higher than 620,000 and IRR ranging from 0% to 4%.

34


Page 35 of 60

×10 5

50

0

60 0

14

NH3 p (US$/ton)

70

0

12

10

POP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


8

0

60

0 50 6

80

0

0 70

4

0 100

800 700 800

2 0

1

1000 2

3

4

5

6

1400 7

8

9

10

IRR (%)

Figure 13: RTEA 4. Population size versus internal rate of return. GAc at 100% its actual price. NH3 minimum selling price calculated with NPV equals to zero. The plant becomes competitive for a population higher than 550,000 with an IRR from 0% to 4% (Figure 14) and for a population higher than 450,000 with an IRR from 0% to 7% (Figure 15) with a decrease of 25% and 50% of GAc , respectively.

35



×10 5

50

0

400

60 0

14

NH3 p (US$/ton)

70

0

12

POP

10

8

0 80

0 60

0

50

6

0 70

0

4

100

800

700 800

2 0

1

1000 2

3

4

5

6

1400 7

8

9

10

IRR (%)

Figure 14: RTEA 5. Population size versus internal rate of return. GAc at 75% its actual price. NH3 minimum selling price calculated with NPV equals to zero.

×10 5 14

NH3 p (US$/ton)

50

0

40

0

60 0

12

10

POP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 60

8

0 70 0

60

6 0 50

800

4

700 600

800

700 800

2 0

1

1000 1000

2

3

4

5

6

7

8

1400 9

10

IRR (%)

Figure 15: RTEA 6. Population size versus internal rate of return. GAc at 50% its actual price. NH3 minimum selling price calculated with NPV equals to zero. Although the plant is currently competitive only with a low IRR and for cities with a population higher than 620,000, with a reduction of the GAc , better results can be achieved. 36


Page 37 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


For cities with small populations, consortia could be formed in order to achieve the plant required production capacity 9 . In addition, this kind of plant requires more than 100 employees to be operated, creating job opportunities. Other advantages of producing NH3 from MSW gasication are the usage of a non-fossil fuel source as raw-material, the MSW is treated properly and emissions of CH4 due to MSW decomposition are inexistent. However, care must be taken during the interpretation of the nal economic result. Since NH3 is supposed to be produced in a decentralized manner and the transportation costs are not considered in this analysis, its nal selling price could considerably increase if it must be transported to regions far from the plant. This would make the economic feasibility of the project more dicult to be achieved.

Conclusion In this work, a RTEA of plant designed to treat MSW generated by 100,000 people was carried out. According to the selected compressor driver, namely, steam turbines, internal combustion engine and electric motor, three energetic arrangement were proposed and optimized. The best option was found to be the internal combustion engine. For this arrangement, the production capacity was 21.21 ton of NH3 per day. For an IRR of 15% and a project life of 20 years, the NH3 minimum selling price was calculated to be US$ 2,350 per ton. This value is considerably higher than the average market price from last decade. Reducing the IRR and increasing the plant capacity 15 times, the NH3 selling price calculated was still not competitive. Therefore, the plant is not currently attractive for private investors. However, the plant was found to be competitive if installed to treat MSW generated by 620,000 people or more with an IRR ranging 0% to 4%. This scenario is considered good for municipalities to invest. To operate with such a low IRR is not a bad scenario for the municipalities because they spend billions yearly on MSW treatment and this scenario would help to reduce the expenditure. This scenario could also be improved with the reduc-

37



Page 38 of 60

tion of the gasier installed cost. In addition, for each installed plant, more than 100 jobs would be created. It would promote recycling, generate NH3 from a non-fossil fuel source and minimize CH4 emissions due to MSW decomposition.

Acknowledgement The authors thank the nancial support provided by the Coordinating Body for the Improvement of Postgraduate Studies in Higher Education (CAPES) and by the Research Support Foundation of the Rio de Janeiro State (FAPERJ).

Supporting Information Available PlantModels.zip: Compressed folder that contains the three EMSO models, i.e., Plant1.mso, Plant2.mso and Plant3.mso, used to perform the analysis. It also contains the guess les used to facilitate the plant models simulations and a EMSO model for streams, i.e., streams1.mso. Supporting Information for Publication.pdf: Contains the thermodynamic data used as input for the gasier model and informations that might be relevant for the reader. This information is available free of charge via the Internet at http://pubs.acs.org/.

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Page 45 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Graphical TOC Entry S5 GA S1

ACB

S7 HE1

S14

ZB

BF S11

S12

S13 MX2 S15

S6 PSA

S22 S8

S4

ISR

CP1 S10

S9

S20

KV1

S21

MX3

HE2

CND1

MX1

S3

S18

S17

SP1 S2

CP2 BL

HE3 S26

AMR

S19

S33

S24 S25 MX4 S35

S23 S32

S34

SP2

S28

KV2 AST

CP3

HE4 S27

S29

CND2

45

S30 S31


S16

S5

ACB

S7

S14

ZB

BF

ISR

CP1

Industrial & Engineering Chemistry Research Page 46 of 60 GA

HE1

S1

S10

S9

S12

S13 MX2 S15

S6

S22 S8

S4

PSA

S20

KV1

S21

MX3

HE2

CND1

MX1

S3

1 2 3 4

S11

S18

S17

SP1 S2

CP2 BL

HE3

AMR

S19

S33


S26

S24 S25 MX4 S35

S23

S32

S34

SP2

S28

KV2 AST

CP3

HE4 S27

S29

CND2

S30 S31

S16

Molar flow (kmol/h)

a

Page 47 of 60

ER

Molar fraction

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41


80

H2

60

CO CO2

40

H2O

20

CH4

0 650

H2S

700

750

800

850

900

950

1000 1050 1100 1150

Gasifier temperature (K) b 0.3

H2

0.2

CO CO2 H2O

0.1 0 650

CH4 H2S

700

750

800

850

900

950

1000 1050 1100 1150

Gasifier temperature (K) c 0.6 0.4 0.2 650

700

750

800

850 900 950 1000 1050 1100 1150 ACS Paragon Plus Environment

Gasifier temperature (K)

a

Molar fraction

0.12

Page 48 of 60

b

750

Temperature (K)

NH3 molar fraction

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41


0.1 0.08 0.06 0.04 620 645 670 695 720

700 650 600

Temperature (K)

0

4000 8000 12000


0.8 0.6

H2 N2

0.4

NH3

0.2 0

0

4000 8000 ACS Paragon Plus Environment

Catalyst mass (kg)

12000

Page 49 of 60

Molar fraction

0.15

b

800

Temperature (K)

NH3 molar fraction

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

a


0.1 0.05 0 580

640

700

750 700 650 600 550

760

Temperature (K)

0

4000 8000 12000


0.8 0.6

H2 N2

0.4

NH 3

0.2 0

0

4000 8000 ACS Paragon Plus Environment

Catalyst mass (kg)

12000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Predicted (kmol/h of H 2 )


600 500 400 300 200 100 100

Ideal Predicted

200

300

400

500

Simulated (kmol/h of H 2 )


600

Page 50 of 60

Page of 60 & Engineering Chemistry Research 80051Industrial

Predicted (K)

780 1 2 3 760 4 5 740 6 7 720 8 9 10700 11 12680 13 680 14

Ideal Predicted

ACS Paragon Plus Environment 700 720 740 760 780

Simulated (K)

800

20

×Industrial 104 & Engineering Chemistry Research Page 52 of 60

Predicted (kg)

1 15 2 3 4 5 10 6 7 8 5 9 10 11 12 0 13 0 14

Ideal Predicted

ACS Paragon Plus Environment 5 10 15

Simulated (kg)

20 × 104

ACS Paragon Plus Environment an

r

bo

La tra

is

in

m

ad

ce

an

es

iti

til

nt en

U

tiv Be d e in ne su fit ra s nc an e R d es tra ea in Su rc in h g pe a C nd rv a t is al de io ys n ve t an lo pm d m Se en an llin t ag g em an en d m t ar ke R tin aw g m at er ia l

s

d

an xe

Ta

al

er

en

G

M ai

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Production Costs (US$)

Page 53 of 60 ×10 5 20 Industrial & Engineering Chemistry Research

15

10

5

0

-5


-0.8

-0.8

POP ACB a

-0.85

NPV normalized

-1 -1.05

-1 -1.05 -1.1

-1.15

-1.15 -10

0 %

10

20

-1.2 -30


30

GAc

-0.95

-1.1

-20

IRR AMRc

-0.9

NH3p

-0.95

-1.2 -30

MSWp

-0.85

PSAr

-0.9 NPV normalized

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

Page 54 of 60

-20

-10

0 %

10

20

30

Page 55 of 60 20

Industrial & Engineering Chemistry Research 280

0

19

22

00

240

0

18

260

0

17

IRR (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

16

200

220

0

240

0

0

15 200

14

220

0

0

13

NH3 p (US$/ton)

180

0

200

12

0

11 160

10 50

55

60

0 ACS Paragon Plus Environment 65 70 75 80 85

Ga c (%)

180

0

90

95

100


×10 5 14

NH3 p (US$/ton)

12

10

00

80

0

00

12

10

POP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 56 of 60

8 00

14

00

6

12

00

10

0

140

4

0 120 2 10

1400 11

12

13

1800 2200 14 15

1800 2200

16

IRR (%)


17

18

19

20

Page 57 of 60 ×10 5

NH3 p (US$/ton)

14

12

0

80

0

00

90

10

10

POP (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22


8

900

0

100

1200

6

1000 1200

4

1400

1400

2 50

1800 55 60


GAc (%)

90

95

100


×10 5

50

0

60 0

14

NH3 p (US$/ton)

70

0

12

10

POP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Page 58 of 60

8

0

60

0 50

0

80

6

0

70 4

0

100

800 700 800

2 0

1

2

1000 ACS Paragon Plus Environment 1400 3 4 5 6 7

IRR (%)

8

9

10

Page 59 of 60 ×10 5

50

0

400

60

0

14

NH3 p (US$/ton)

70 0

12

10

POP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22


0

8

80

0 60

0

50

6

0

70

0

4

100

800

700 800

2 0

1

1000 2


IRR (%)

8

9

10


×10 5 14

NH3 p (US$/ton)

50 0

40

0

60

0

12

10

POP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Page 60 of 60

8

0

70 0

60

6 00

5

4

800 700

600

700 800

2 0

1

800

1000 1000ACS Paragon Plus Environment 2 3 4 5 6 7

IRR (%)

8

1400 9

10

Preliminary Design of a Municipal Solid Waste (MSW) Biorefinery for

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