Ind. Eng. Chem. Res. 2004, 43, 7785-7795
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PROCESS DESIGN AND CONTROL Modeling the Energy Consumption of Chemical Batch Plants: Bottom-Up Approach Patric S. Bieler, Ulrich Fischer,* and Konrad Hungerbu 1 hler Safety & Environmental Technology Group Institute for Chemical- and Bioengineering, Swiss Federal Institute of Technology (ETH), CH-8093 Zu¨ rich, Switzerland
In a previous paper, the modeling of chemical batch plants with the help of a top-down model was presented (Bieler et al. Ind. Eng. Chem. Res. 2003, 42, 6135-6144). This approach was not applicable to multipurpose batch plants with highly varying production because of the high variability between products and the complexity of such plants. In the present paper, a bottomup model is proposed, and its applicability to a multipurpose batch plant is investigated. Extensive measurements of single apparatus and unit operations were conducted, leading to simple, adaptable models for the consumption of steam, electricity, and brine. These singleapparatus and single-unit-operation models are summed according to the production schedule and the corresponding process step procedures or the production records. This leads to a bottomup model of a complete multipurpose production plant. The energy consumption of the whole plant was modeled for 1 and 2 days, as well as for 1 week and 1 month. The comparison of the bottom-up modeling results with the measurements on the building level showed good agreement and demonstrated the applicability of the proposed approach. It was therefore possible to analyze the consumption of the different energy carriers in more detail on the basis of the bottom-up modeling results. The energy consumption of individual process steps, products, apparatuses, and apparatus groups was analyzed. This analysis revealed the most important saving potentials in the investigated plant and helped to focus optimization efforts. Furthermore, the bottom-up approach enables a proper allocation of energy consumption to different products in a multipurpose batch plant. 1. Introduction About 75% of all manmade air pollution is caused by energy use.2 Therefore, energy optimization and minimization has a direct impact on the environmental friendliness of an industrial process. About 50% of industrial processes3 and chemical production processes4 worldwide are batch processes. Energy costs amount to about 5-10% of total production costs for common chemicals produced in batch operation.5 A helpful overview of energy consumption and management in batch production is provided by Grant.6 Some authors have reported that significant savings in energy costs (and consumption) in batch plants of up to 20% in process heating are possible and verified this assertion by means of detailed case studies.7,8 Other authors have found CO2 reduction potentials of up to 10% for batch plants.9 General methodologies for energy analysis in batch production are, nevertheless, unavailable. In contrast to continuous processes, studies on energy consumption or energy saving potentials for batch processes are limited, and corresponding methods are not yet well-established.10-16 Furthermore, such studies are often focused on heat integration17,18 and therefore rely on available storage capacity or constant production * To whom correspondence should be addressed. Tel.: +41 1 6325668. Fax: +41 1 6321189. E-mail: ufischer@ chem.ethz.ch.
schedules. Other studies account for time-varying temperatures19 and rescheduling.20 The use of these methods in batch production is limited because most of them are considered too complicated, lengthy, demanding, and complex to be of practical interest for most of the cases encountered.3 Further developments of the pinch analysis mentioned above are the time average model (TAM) and the time slice model (TSM). These models were introduced by Linnhoff et al.21 and further used by several authors.3,22,23 Both the TAM and the TSM adapt the concept of pinch analysis introduced by Linnhoff et al.13 to batch processes by averaging the energy consumption either over the whole production time (TAM) or over several time steps during a batch production (TSM). No models are available in the literature to compute the energy consumption of multipurpose batch processes, accounting for the consumption caused by the chemical process itself, the consumption due to the equipment specification, and especially the losses of the different apparatuses. The top-down model1,24,25 presented earlier provides the possibility of energy modeling on the building level. In this top-down model, the energy consumption of a chemical batch plant is modeled by linear equations correlating the amounts of chemicals produced with the utility consumption in the form of steam, electricity, or brine. A detailed description of the top-down model is
10.1021/ie049641j CCC: $27.50 © 2004 American Chemical Society Published on Web 10/22/2004
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Figure 2. Concept of the bottom-up model. Figure 1. Generic structure of a multipurpose batch plant.
given in Bieler et al.1 The top-down model proved not to be applicable to multipurpose batch plants with highly varying production. For these plants, this paper presents for the first time an easy-to-use and adaptable model based on single unit operations (SUOMs). This new approach is similar to the TSM in the regard to the splitting of a batch production into several slices (e.g., heating, reaction, etc.) and the separate modeling of the energy consumption of these slices. Nevertheless, the focus here lies not on pinch analysis but on analysis of the whole production process for allocation and optimization of energy consumption in the form of steam, electricity, and brine. The model results can be aggregated for different levels of analysis, showing different aspects of the energy consumption of the investigated building in detail. The model can be used to reveal the energy saving potentials of production plants as well as to properly allocate energy costs to different products in a multipurpose batch plant. 2. Bottom-Up Model A generic multipurpose batch plant consists of a batch production equipment part, a production and building infrastructure part, and special equipment.1 External suppliers (contractors) are responsible for providing and recovering solvents and for providing energy carriers (see Figure 1). 2.1. Concept of the Bottom-Up Model. The energy consumption of such a production plant is split into infrastructure consumption and production-dependent consumption (see below). Thus far, this approach is a similar concept as the top-down model.1,25 Beyond the top-down model, the bottom-up model analyzes the production-dependent part in more detail. The energy consumption Em of one specific energy form (subscript m ) in a whole plant per period is described as
Em ) E Pm + ETImt
(1)
where E Pm is the production-dependent consumption of one energy form (subscript m) per period (including the batch production energy consumption, the energy consumption of the special equipment, and the productioninfrastructure energy consumption), ET Im is the consumption of one energy form (subscript m) by the
building infrastructure (superscript I) per time unit (usually including ventilation, heating, lighting, personal computers, warm water preparation, etc.), and t is the length of the period in seconds per period. The building-infrastructure energy consumption is specific for each plant and is measured or calculated on the building level. In the bottom-up model, the production-dependent energy consumption E Pm comprises different forms of energy, production in different apparatuses, and the production of different chemicals each defined by a process step procedure (PSP). As shown in Figure 2, this leads to a split of the energy cube. Each cubicle section P describes the energy consumption per batch of one E ijm specific chemical (subscript i), produced in one specific apparatus (subscript j ), and requiring one specific energy form (subscript m) A L E Pijm ) E CM ijm + E ijm + E ijm
(2)
CM is the chemical mass (superscript CM) where E ijm dependent energy consumption of one specific energy carrier for one specific recipe in a specific apparatus per A is the (empty) apparatus (superscript A) batch; E ijm dependent consumption of one specific energy carrier L is the timefor one specific PSP per batch; and E ijm dependent loss and motor term (superscript L) for one specific energy carrier, one specific apparatus, and one specific PSP per batch. Below, these terms are discussed in more detail. CM , The energy consumption of the chemical mass, E ijm is defined by the equation
E CM ijm ) Fm
∑q ∑k (cPkmijk∆Tiq + mijkq∆Hik)f
(3)
where Fm is a dimensionless factor defining use (Fm ) 1) or nonuse (Fm ) 0) of a specific energy form; cPk is the heat capacity, mijk is the mass, ∆Tiq is the temperature difference, and ∆Hik is the enthalpy (of vaporization, reaction, melting, etc.); the index k refers to the different chemicals used in the step; the index q refers to the different process steps (e.g., temperature levels, unit operations) of the specific recipe; and f is a conversion factor of 1/(3600 s/h) to convert kilojoules into kilowatt-hours. The energy consumption dependent on the specificaA , is described by tions of the apparatus, E ijm
Ind. Eng. Chem. Res., Vol. 43, No. 24, 2004 7787
∑q ∑n cPnmjn∆Tiqf
E Aijm ) Fm
(4)
Table 1. Summary of the Equations and Parametersa for the Different SUOM parameters and values
where the index n refers to the different aspects (i.e., materials, cooling liquid) of an apparatus. The temperature difference represents the difference between the starting temperature of the jacket of an operation and the final temperature. It is assumed that the whole vessel reaches the temperature of the jacket. Heating of the insulation is neglected. The time-dependent losses and the consumption of L , is exelectric motors (superscript L) per batch, E ijm pressed by time-dependent equations of the type
E Lijm )
∑q (KjmAj∆Tiq - ηjγj P Nj )tijmqf
(5)
(6)
∑j ∑i ni E Pijm
(7)
∑ ∑∑ ni E Pijm m j i
(8)
E Pm ) EP )
∑j E Pijm
The production-dependent energy consumption given in eq 8 can be substituted into eq 1 to obtain the total energy consumption of a production plant. For the application of the bottom-up model, the parameters of the abovementioned equations have to be determined with the help of measurements, as discussed by Bieler.25 2.2. Detailed Apparatus Models. In this section, examples of eq 2 for important apparatuses are given (see Bieler25 for more details). The electricity consumption (subscript El) of electric equipment is strongly related to its nominal power. The nominal power is a physical property describing the motor. The following equation was used P ) γPN∆tf E i,j,El
(9)
where γ is the part of nominal power consumed by the
utility
η γ C modeling K eq no. [kW/(m2 K)] (%) (%) (kW) 28 28 - 100 85 28 -
reactor
steam brine electricityb electricityc electricityd
11 11 10 9 9
4.2 × 10-2 1.7 × 10-2 -
60 60 -
Nutsche dryer
steam electricityc electricityd
11 9 9
4.2 × 10-2 -
60 28 - 85 - 28
-
heat chamber
steame electricity
12 9
4.2 × 10-2 -
- - 64
-
electricity
9
-
- 52
-
electricity brine
9 10
-
- 62 - -
30
steam jet pump
steam
10
-
-
-
93
stirrer and motorf
electricity
9
-
- 28
-
infrastructure and losses
electricity steam brine
10 10 10
-
-
short-path brine distillation column electricity
10 9
-
- - 96
falling-film evaporator
steam electricity
11 9
4.2 × 10-2 -
60 85 - 85
-
horizontal vacuum rotary dryer
steam electricityc electricityd
11 9 9
4.2 × 10-2 -
60 28 - 85 - 28
-
batch distillation column
steam electricity
13 9
2.5 × 10-2 -
60 28 - 28
-
vacuum pump APOVAC pump
where Kjm is the heat-transfer coefficient of the apparatus, Aj is the total outer surface area of the apparatus, ∆Tiq is the temperature difference between the ambient temperature and the heating jacket, P N j is the nominal power of the motor, γj is the ratio of the actual power of the motor to the nominal power consumption, ηj is the efficiency of the motor (i.e., correlation of the power actually delivered by the electric equipment to the actual power consumption of the corresponding apparatus), and tijmq is the operating time of one specific process step in one specific piece of equipment, producing one specific product, and requiring one specific energy form. Equations 3-5 are substituted into eq 2. This results in the basic equation for the bottom-up model as depicted in Figure 2. With the help of the number of batches (ni ) of one chemical produced in a certain period, summations are possible leading to different levels of aggregation (e.g., the energy consumption, E Pm, of one specific energy form for the production of all chemicals in all apparatuses) and finally to the total production-dependent energy consumption EP
E Pim ) ni
apparatus
g
- 180 - 200 - 20 3.6 -
a Bold values are absolute values, especially specific for the apparatus of the investigated building. b For heating of the hightemperature reaction vessel. c Circulation pump. d Additional equipment related to the main apparatus. e Other fixed values: cSP ) 2.5 kJ/(kg K), mB ) 1.2 t (see ref 25). f For all apparatuses. g See, e.g., http://www.rosenmund.com/vacsyssolvrec.html.
equipment, PN is the nominal power of the equipment, and ∆t is the time of operation of the equipment. The general model for an apparatus with constant consumption (e.g., steam jet pumps; see Table 1) is proposed according to the following equation P E i,j,m ) Ctf
(10)
P is the consumption of the specific energy where E i,j,m form (index m; i.e., steam, electricity, brine), C is a constant consumption per time of the specific energy form in kilowatts, and t is the operation time. Combining eqs 3-5 results in the detailed eq 11 describing the steam consumption (subscript St) of a batch reaction vessel (subscript RV). The heating/cooling system (H/C system) of the reaction vessels investigated is a so-called pressure water system, where the vessel is heated by circulating water in the heating/cooling system. This water is heated by steam directly inserted into the heating/cooling system
P RM ES ) {[mRM(cRM E i,RV,St P ∆TRM + ∆H R ) + mES∆H V ] +
[(mAcAP + mWcW P )∆TA] + (KA∆TAm - ηγPN)t}f (11) P is the production-dependent steam conwhere E i,RV,St sumption (either 5 or 15 bar) of a reaction vessel; m represents the masses of the reaction mass, the evaporated solvent (subscript ES), the apparatus, or the water in the heating/cooling system (subscript W); cP repre-
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sents the heat capacities of the reaction mass, the material of the apparatus, or the water of the heating/ cooling system; ∆T represents the temperature increase of the reaction mass or the apparatus or the temperature difference between the apparatus and the ambient temperature (subscript Am); ∆HR is the reaction enthalpy (note that negative values of ∆HR represent exothermic reactions); ∆HV is the heat of vaporization; K is the loss coefficient; A is the surface area of the vessel, η is the efficiency of the stirrer motor; γ is the ratio of the actual power to the nominal power consumption of the stirrer; PN is the nominal power of the stirrer motor; and t is the batch time. A similar model was proposed for heat chambers (subscript HC) that are used for heating barrels of chemicals P A Air E i,HC,St ) [mBcW P ∆TB + (mAcP + mAircP )∆TA + KA∆TAmt]f (12) P where E i,HC,St is the production-dependent steam consumption of the heat chamber, mB is the mass of the filled barrel, cSP is the assumed heat capacity of the organic content of the barrel [2.5 kJ/(kg K)],26 ∆TB is the temperature rise of the barrel and its contents, mA is the mass of the heating chamber (apparatus), cAP is the heat capacity of stainless steel, mAir is the mass of air inside the heating chamber, cAir P is the heat capacity of air, ∆TA is the temperature rise of the heat chamber, K is the heat-transfer coefficient to the environment (loss coefficient), A is the surface area of the heat chamber, ∆TAm is the temperature difference between the outside wall and the environment, and t is the operation time. Targeting the total energy consumption in a batch distillation column and neglecting dynamic effects, the proposed model is similar to the model for a batch reactor (see eq 11), except that no reaction is occurring and that, for the distillate, the reflux ratio has to be considered in calculating the required energy consumption. Furthermore, the investigated batch distillation column was heated by condensing steam in the heating/ cooling system without water circulation. Therefore, the heat required for heating the contents of the pressure water system (see eq 11) is missing in eq 13
P ) {[mS(cSP∆TS) + (1 + RR)mD∆H D E i,BC,St V] +
mAcAP ∆TA + (KA∆TAm - ηγPN)t]f (13) P where E i,BC,St is the production-dependent steam consumption of a batch column; m is the mass of the solvents (subscript S), the distillate (sub- or superscript D), or the apparatus; cP represents the heat capacity of the initial solvent mixture or the material of the apparatus; ∆T represents the temperature increase of the solvents or the apparatus or the temperature difference between the apparatus and the ambient temperature; ∆HV is the heat of vaporization representing the composition of the distillate; RR is the dimensionless reflux ratio; K is the loss coefficient; A is the surface area of the vessel; η is the efficiency of the stirrer; γ is the ratio of the actual power to the nominal power consumption of the stirrer; PN is the nominal power of the stirrer; and t is the batch time.
Figure 3. Layers of the program for modeling the energy consumption of chemical batch plants.
3. Case Study: Application of the Bottom-Up Model to a Multipurpose Batch Plant The investigated building is a multipurpose batch plant with 29 major apparatuses (typical volume of the reaction vessels is about 6 m3). It shows a high variability between products and a high change in production mix over the year, the reaction temperature varies from below -10 °C to above +200 °C, and the monthly production of chemicals is about 400 t for a typical month of production (i.e., building 1 in ref 1). Mainly organic solvents are used. Extensive measurements were carried out in this plant. For electricity consumption, the basic measurements were current and voltage, and steam and brine consumption were measured in the form of flow rates. These basic measurements were then transformed into the units of energy consumption used in the model, namely, kilowatt-hours, by integrating over time and considering the density, heat capacity, and temperature change for brine and the heat of vaporization for steam; for electricity consumption, this conversion was done by a data logger (see Bieler25 for further details). The heat of vaporization for both 5 and 15 bar steam (including cooling of the condensed water to average jacket temperature) was set to 0.65 kWh/kg according to values given in Lide26 and discussions with industry experts. The approach outlined above targets the energy content of the utilities and not the total energy that is required to generate the utilities. Using these measurements, the parameters of the corresponding bottom-up model were determined. The resulting parameter values are summarized in Table 1. Measurements were not possible for all apparatuses available in the building, and a number of assumptions had to be made.25 Furthermore, assumptions were required to keep the models easy enough to be of use for daily business. The investigated apparatuses are mainly DIN standard vessels.27 This makes the results transferable to other buildings where the same standard equipment is used. Investigations on the single-apparatus level led to the conclusion that simple models according to the base eq 2 are applicable to modeling of the energy consumption of these apparatuses. The modeling of the whole plant was performed using an Excel model28 (called a program in the remainder of this paper). The program is split into four layers as shown in Figure 3. The base data layer consists of the specifications of the standard substances, the apparatuses used, and the general modeling parameters given in Table 1. The production data layer contains the input of the production data either from production records (PRs) or from process step procedures (PSPs). The calculation layer calculates the different energy consumptions according to the models presented in the
Ind. Eng. Chem. Res., Vol. 43, No. 24, 2004 7789 Table 2. Example of a Generic PSP and Its Translation for the Data Input to the Programa amount task
PSP
check whether vessel is empty make vessel inert input solvent S start stirrer (stage 1) input reactant A input reactant B put stirrer to stage 2 heat to 70 °C react and hold at 70 °C take sample and send to lab put stirrer to stage 1 evaporate byproduct C check transfer pipe cool to 30 °C transfer mixture end
time
modelb
PSP
model
-
*
-
-
s kg a kg b kg c kg -
s kg * a kg b kg -c kg -(s + a + b - c) kg *
3h -
15 min 15 min 15 min 45 min 3h 2h 45 min 30 min -
a Data required for the program input are printed in bold face. An asterisk (*) signifies that the step is mentioned in the model without a time duration.
b
Table 3. Investigated Periods period name
starting time
ending time
1 day 2 days 1 week 1 month
04.05.2003 06:00 04.05.2003 06:00 05.05.2003 06:00 06.01.2003 06:00
05.05.2003 06:00 06.05.2003 06:00 12.05.2003 06:00 10.02.2003 06:00
preceding section. The results are finally summarized and presented in the results layer. An example of a generic PSP and its translation to input data for the program is presented in Table 2. The description of the process in a PSP is rather detailed. For the data input to the program, such a degree of detail is not required. Only the most important process steps are required for the program, such as inputs or removals of substances, the heating or cooling of the reaction media, and holding times between steps, have to be provided. This reduces a long PSP to a few lines in the worksheets of the production data layer. This is, nevertheless, a significant effort for even a small number of days in a medium-sized building if no electronic form of the PSP (or PR) is available (as in the investigated building). The times not given in the PSP (e.g., required for the input of substances; see Table 2) were found by discussions with production personnel or by measuring the corresponding times of several batches and computing the average time. It is also possible to compute the operation times according to theoretical data as discussed in the Conclusions and Outlook section. When modeling a longer period than a couple of days (e.g., a week or a month), the modeling has to be performed with data extracted from the PSP. Therefore, a file (i.e., one Excel workbook) is built for each product produced during the investigated period. As result of each file, E Pim as defined in eq 6 is obtained. 4. Results and Discussion The results from the application of the bottom-up model to the investigated plant and the analysis thereof are presented in the following sections. Different periods (see Table 3) were used to compare results obtained with the bottom-up model and measurements in the plant. For the periods of 1 and of 2 days, modeling according to PR and PSP data was performed. The exact produc-
tion data (extracted from the PR as mentioned above) can be used as input. This is tedious and time-consuming even for short periods if no electronic version of the PR is available.28 Therefore, this approach is suitable only for showing the accuracy of the model by comparing modeled results and measured data over short periods as discussed below. For the longer periods, only the PSP data were used as input. In the following discussion, when results for the period of 1 week or 1 month are considered, PSP data were used as model input in all cases. The data extracted from one PSP are only entered once. As shown in eq 8, the modeling of the whole plant can then be performed by multiplying the consumption of one batch by the number of batches produced for each product during the investigated period. The accuracy of the bottom-up model of a building depends on the accuracy of the single-unit-operation models presented in the equations above. The actual bottom-up model (i.e., the model of the whole plant) can only be tested on building level because of the lack of routine measurements of smaller parts of the investigated plant. The plant model incorporates data not only for each different chemical produced, but also for some special operations such as reconcentration and distillation of the used brine; ethanol distillation in the falling-film evaporator; and decalcification, cleaning, and preparation of the reaction vessels. These tasks are important for the total energy consumption of the plant and are also included in the bottom-up model. 4.1. Model Testing. In this section, the modeled consumption of the different energy carriers for the whole building, different periods, and different data input (i.e., either from PRs or from PSPs) is compared to measurements. The model output is according to the E Pm presented in eq 7. For one common day of production, the results are depicted in Figure 4. For the specific consumption, the modeled consumption was divided by the total amount of products actually requiring the specific utility, whereas the base consumption was divided by the total amount of chemicals produced. The consumption of the whole building was calculated not only with the bottom-up model using PRs as well as PSP data but also with the utility calculation method used by the company at which this case study was conducted (called the CPM, or company proprietary method). The result obtained with the CPM is the most inaccurate one (Figure 4). The CPM model is based on experience in daily production. The main interest is to give an approximate number of the (total) product cost. The detailed bottom-up model delivers much more accurate results than the CPM. The model results based on data extracted from the PRs deviate only slightly from the measured values. This fact indicates the accuracy of the model. The result obtained with the PSP data deviates more from the measured value for the 1-day period. This has several reasons. First, it is based on the standard values of the PSP. These standard times, temperatures, masses, etc., do not agree fully with the actual ones. This implies a deviation inherent in the model especially for shorter periods, where different deviations from the standard parameter values do not even out. Furthermore, the model based on PSP data requires the number of batches produced (see eq 8). The different parts of the batches of one product
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Figure 4. Comparison of calculations and measurements of the specific utility consumptions (per ton of product) for the whole building and 1 day of production (see Table 3). Table 4. Relative Deviations between the Measurements and the Different Modeling Methods for the Investigated Utilities modeling method
steam (%)
electricity (%)
brine (%)
1 day
PSPa PRa CPM
16 -2 175
14 3 -12
-12 -18 -67
2 days
PSPa PRa CPM
8 -3 147
5 0 -19
-19 -5 -70
1 week
PSPa CPM
1 138
-2 -28
-27 -73
1 month
PSPa CPM
-5 145
5 10
-16 -51
period
a
Calculations were performed according to eq 1.
under production during the investigated period were therefore summed up to result in virtual batches. These virtual batches were used for the modeling (i.e., consumption of one standard batch times the number of virtual batches using eq 8). An only slightly larger deviation at shorter periods indicates that the model in combination with PSP data should be applicable for longer periods, which were the focus of the investigations. The relative deviations between the measurements and the different models for several periods are presented in Table 4. Brine consumption shows the largest deviation between the bottom-up model and the measured values. This is explained by the difficulties and high inaccuracies inherent in the corresponding singleapparatus measurements.25 In Figure 5, the deviations for the CPM are again as high as those in Figure 4. The deviations of the PSPbased model results are smaller for the longer period. If the assumption that the batch time given in the PSP equals the mean time of the batch operation is correct,25 the variations in the processing times of different batches level out over long periods. For the modeling of brine, the deviations from the measured values are of the same order of magnitude for all of the investigated periods. This is because of the large uncertainty in the models of the single unit operations based on uncertain measurements conducted for the determination of model parameters.25 Given that
the measurements are always higher than the modeled values, it could also be a hint that the loss coefficient is larger for a standard batch vessel than the results of the measurements of the single unit operations indicate. Another possibility could be that the assumption of a minor contribution of safety cooling systems of some batch reactors was not correct and that these consumptions are higher than expected. Because of the impossibility of measuring these equipment units in the investigated plant, this could not be proven. The enthalpy of crystallization, which was neglected in the model, could be another small contribution to the brine consumption. Overall, it is concluded that the bottom-up model delivers accurate results of the total energy consumption of the investigated multipurpose batch plant. The model is therefore applicable as a tool for analyzing the energy consumption of such batch plants in more detail. This is discussed in the following section. 4.2. Analysis of the Energy Consumption of the Investigated Multipurpose Batch Plant. The bottom-up model offers the possibility of analyzing the energy consumption of the investigated building in more detail using eqs 1-8. Analysis of Different Apparatus Groups. The apparatus groups and special aspects of the production in the investigated production plant include the following: the reactors and Nutsche dryers, the heat provided (or required) by the enthalpy of reaction, the consumption of the heat chamber, the steam jet pumps, the external vacuum pumps (the APOVAC pumps are considered directly with the Nutsche dryers, whereas the general vacuum pumps are considered as part of infrastructure consumption) and the base consumption (building infrastructure). Figure 6 presents the model results for the steam consumption of 1 month and the different apparatus groups. The absolute value of the steam consumption of the reactors and Nutsche dryers is the largest. These apparatuses should therefore be investigated in more detail (see below). Furthermore, it can be seen that the base consumption is small as no infrastructure equipment is using steam. The steam jet pumps also have no significant influence on total steam consumption. These machines are working only when required and are shut down if not in use.
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Figure 5. Comparison of calculations and measurements of the specific utility consumptions (per ton of product) for the whole building and for 1 month of production.
Figure 6. Absolute modeled steam consumption of different apparatus groups during 1 month.
A picture similar to that presented in Figure 6 for the absolute modeled steam consumption is found for the specific steam consumption.25 The specific steam consumption is calculated by dividing the absolute modeled steam consumption for each product by the produced mass of this product. Base consumption is divided by the mass of all the chemicals produced in the investigated period. A summary of the investigations on electricity and brine is given below (also see ref 25 for more details). Analyzing the Steam Consumption of the Reactors and Nutsche Dryers. Because the apparatus group “reactors and Nutsche dryers” was identified as the most important steam consumer, it is now investigated in more detail. Figure 7 presents the modeled specific steam consumption of the reactors and Nutsche dryers. The different unit operations requiring steam, the stirrer input, and the losses are investigated for 1 week and 1 month, both based on PSP data. The different production mixes during the two periods result in different modeling results for the specific steam consumption because different products require different amounts of steam. Reflux conditions are used more often during the investigated month than during the investigated week,
as can be seen in Figure 7. The heating of the apparatuses led to a significantly higher specific steam consumption during the investigated month than during the investigated week. This is reflected in the higher specific losses in Figure 7 as well. Longer heating periods and higher process temperatures have an influence on both the loss coefficient and the heating of the apparatus. The specific consumption of the apparatus heating and the heating/cooling system could both be improved by moving products from smaller apparatuses to larger ones. This improves the relation between the outside surface area of an apparatus and its contents. Because the weight of an apparatus (metal) is related to its surface area (surface area times thickness of the metal times the density of the metal equals the weight of a reactor), the specific energy consumption for heating the apparatus is decreased by increasing the size of an apparatus. The heating of the heating/cooling system uses less specific steam during the month than during the week. This shows that different apparatuses were in use during the two periods. The apparatuses used during the week were probably reactors with thinner walls but with the same water content of the heating/cooling systems as the reactors with the thicker walls. Because
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Figure 7. Specific steam consumption of the different aspects in the apparatus group reactors and Nutsche dryers for two periods and PSP data.
Figure 8. Specific steam consumption of 13 different products, A-K, N, and O, calculated using eq 6 and PSP data [the calculations were performed for 1 week (W) and for 1 month (M)].
of the specifically higher water content of the reactors with thinner walls, the specific steam consumption for the heating/cooling system is increased, and the specific energy consumption for apparatus heating is decreased. Stirrer input (heat of friction introduced into the system by stirrers) can be neglected. It is only about 2% of the specific steam consumption. Losses, nevertheless, are significant and responsible for about 50% of the total steam consumption for the reactors and Nutsche dryers. About 50% of these losses are caused by the losses through the steam traps.25 Heat transfer through the outside wall of the apparatus is responsible for the other 50%. It is obvious that minimization of the losses provides the best possibility for optimizing the steam consumption of the reactors and Nutsche dryers. An improvement of about 10% of the losses would result in a reduction of about 5% of total steam consumption for these apparatuses whereas an optimization of 10% for an improvement of the heating of the substances (i.e., lower process temperatures or solvents with lower heat capacity) would result in only about 1% reduction of total steam consumption.
Focus can also be put on the steam consumption for reflux conditions. It is questionable whether these conditions are always required for the production process (e.g., drying of the solvent) or whether it is only a simple method for keeping the process temperature constant. Detailed investigations of the different PSPs could reveal the actual use of the reflux conditions and lead to an optimization of the steam consumption of this specific process step. Analysis of Energy Consumption of Different Products. In the investigations of the top-down model,1 it was assumed that all of the products of a multipurpose batch plant use about the same amount of specific energy. Using the bottom-up model, this assumption can be analyzed through eq 6 (i.e., calculating E Pim). In Figure 8, the shading of the products gives an even more detailed analysis by presenting different aspects of the production processes as in the previous section. The numbers of synthesis steps required for the different production processes are given as well. The modeling of 1 month and 1 week for two products (G and J) shows the accuracy of the model for both
Ind. Eng. Chem. Res., Vol. 43, No. 24, 2004 7793 Table 5. Sensitivity Analysis of the Bottom-up Model for the Investigated Building Showing the Deviation of the Objective Function Em According to Eq 1 for Changes in the Parameter Values of (20%a steam (%) parameter
15 bar
5 bar
stirrer efficiency stirrer input circulation pump efficiency vacuum pump efficiency heat chamber ventilator efficiency short-path distillation efficiency APOVAC efficiency enthalpy of vaporization (steam) loss coefficient (steam) loss coefficient (brine) Time
(0.3 (0.3 (0.1 (9 (9
(0.3 (0.3 (3 (6 (6-8
a
electricity brine (%) (%) (4 (2 (2 (0.1 -2 (1 (6-7
(1 (1 (3 (9
Modeling period ) 1 month.
periods. Therefore, it is concluded that the differences of total consumption of, e.g., steam for the two periods found in the former sections are caused by differences in production mix and not by inaccurate accounting of the produced amount of chemicals. Figure 8 shows that the assumption of similar specific energy consumptions for all of the different products as proposed in the top-down model1 is not true for the specific steam consumption of the investigated multipurpose batch plant. The products vary widely in the specific steam consumption for the different unit operations required for the production process. The number of synthesis steps given in Figure 8 together with the steam consumption of the different products shows that no correlation is possible. Simple approaches correlating energy consumption with, e.g., the number of synthesis steps are therefore not applicable. Like the different products, the different synthesis steps require different amounts of energy. Top-down approaches are therefore not applicable in such a case. Further Modeling Results. A detailed sensitivity analysis investigating the influence of a number of model parameters on the total energy consumption of the plant was carried out. The results of the sensitivity analysis are summarized in Table 5 (see ref 25 for further details). The sensitivity analysis shows that the influence of most of the parameters is minor. Only the loss coefficients K and the process time t have significant influences on the outcome of the model. The summaries of modeling the energy consumption of the investigated plant for the period of 1 month are shown in Figures 9-11 for steam, electricity, and brine consumption, respectively. In Figure 9, the steam consumption of the investigated building is analyzed with the bottom-up model for the period of 1 month using PSP data. The total modeled steam consumption for this month is 1354 MWh. This is the actual, modeled consumption, as the reaction and stirrer input reduce the modeled consumption by about 80 and about 23 MWh, respectively. The hatched fields in Figure 9 represent the consumptions not directly related to the chemistry of the process (i.e., base consumption, losses, etc.). This consumption is responsible for about 63% of the total steam consumption. Activities for achieving steam savings should therefore concentrate not only on the actual production process but also on the reduction of the base consumption, the losses, the heating of the vessels, etc. It can be seen from Figure 9 as well that the apparatus group
Figure 9. Analysis of the steam consumption (5 and 15 bar) in the investigated plant using the bottom-up model (period ) 1 month, PSP data, total consumption ) 1354 MWh, heat of reaction ) -80 MWh, stirrer input ) -23 MWh).
Figure 10. Analysis of the electricity consumption in the investigated plant using the bottom-up model (period ) 1 month, PSP data, total consumption ) 315 MWh).
Figure 11. Analysis of the brine consumption in the investigated plant using the bottom-up model (period ) 1 month, PSP data, total consumption ) 100 MWh).
consisting of reactors and Nutsche dryers is responsible for the main part of the steam consumption (mainly because of the large losses). Figure 10 presents the modeled electricity consumption for the period of 1 month (PSP data). The total consumption of the modeled month is about 315 MWh. About 50% of the electricity consumption is caused by the building infrastructure (base consumption). This finding corresponds with the findings for different buildings.1 As a general rule, it can be stated, that infrastructure equipment is responsible for about 50% of the electricity consumption of a production building. Optimization and minimization should therefore also include this aspect. The apparatus group reactors and
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Nutsche dryers consumes about one-third of total electricity consumption, and the vacuum pumps specific to processes consume about one-sixth. It is therefore essential to switch-off the vacuum pumps if they are not in use. The largest part of the electricity consumption of the apparatus group reactors and Nutsche dryers is used by the stirrer motors (if no electric heating is performed). Stirrer motors should therefore be checked for optimization potential. By reducing the nominal power of the stirrer motors, the efficiency of the motors can be improved and the electricity consumption reduced.29 The modeled brine consumption for 1 month of operation of the investigated building is presented in Figure 11. Based on PSP data, the total consumption is about 100 MWh for the investigated month. The apparatus group reactors and Nutsche dryers is again responsible for the largest brine consumption (about 72% of total consumption or about 80% of total consumption if the enthalpy of reaction is included). As above (see, e.g., Figure 9), the heat of reaction is modeled and listed separately from the apparatus groups for reasons of transparency. The hatched fields once more indicate the energy consumptions not related to and determined by the chemistry. These consumptions (i.e., base consumption, cooling of apparatuses, losses, and stirrer input) should be targets for optimization or minimization. Together, they are responsible for about 50% of the total brine consumption. Therefore, potential approaches for significant reductions in total brine consumption are revealed. The base consumption of the building (i.e., heat input from the main circulation pumps and losses through the walls of the pipes) is responsible for about one-sixth of the total consumption. This quite significant consumption can be optimized as well. Another main consumer group consists of the APOVAC pumps. Whether these systems really require the use of the low temperatures of the brine or cooling with water would be sufficient should be challenged in further investigations. 5. Conclusions and Outlook This paper presents an algorithm and a methodology for energy modeling of multipurpose batch plants. A good agreement between the results obtained with the bottom-up model and the measurements was found. The model results show where the energy is consumed. With the help of this knowledge, optimization potentials can be revealed. Changes in energy consumption caused by changes in the production mix are also shown and taken into account more accurately than was possible before. Optimization possibilities for the investigated processes were found to lie mainly in two fields: the loss coefficient of the reaction vessels (steam and brine consumption) and the nominal power of the stirrer motors (not shown here; see ref 25). In fact, possibilities for optimization in the investigated plant were found, e.g., in operating stirrer motors closer to their nominal power, in improving insulation and steam traps of the heating/cooling systems, and in improving the complete heating/cooling system. Further optimization possibilities could be found by systematically applying heuristic checklists for batch plants. Although the development of the models required extensive measurements, the models are built to be adaptable to different unit operations, processes, and buildings. It is assumed that, for the modeling of a new
building with new processes but similar equipment, only minor measurements for verification of the models and for determination of the base consumption of the building have to be performed. This is a significant advantage when trying to provide the models company-wide while the basic unit operations and apparatuses stay the same. The model was able to show differences between the different apparatuses in terms of energy consumption as well. The model could therefore be used for comparing the production of the same chemical in different apparatuses. The presented study on the total consumption of the different energy carriers demonstrated that a detailed analysis of an actual production mix is possible with the help of the bottom-up model. This allows the user to identify specific optimization potentials. Focus can be put on the most important unit operations and apparatus groups, and energy targets can be set according to the possible savings found. Furthermore, using the bottom-up model enables a proper allocation of energy consumption and the associated costs to different products. In future research, both the top-down model1 and the bottom-up model will be tested on further production plants. Additional unit operations might then be encountered requiring the postulation of corresponding models as well as the determination of the needed model parameters. In this way, the whole approach will become more general step by step. A case study will also be considered to investigate how the bottom-up model can be used for the design of energy-efficient batch processes. Acknowledgment The authors thank the industrial partner for the good cooperation and the opportunity to investigate existing production buildings as well as for measuring some data. This work was supported by funding of the Swiss Federal Office of Energy (BFE) under Contract 39592/ 79368. Three anonymous reviewers contributed in improving the manuscript. Symbols Acronyms APOVAC ) antipollution vacuum CPM ) company proprietary method H/C system ) heating/cooling system of an apparatus PR ) production record PSP ) process step procedure SUOM ) single unit operation model TAM ) time average model TSM ) time slice model Symbols A ) surface area (m2) C ) constant (kW) cP ) heat capacity [kJ/(kg K)] E ) energy consumption (kWh) ET ) energy consumption per unit time (kWh/s) f ) conversion factor [1/(3600 s/h)] F ) dimensionless factor defining use (1) or nonuse (0) of a specific energy form K ) loss coefficient [kJ/(m2 s K)] m ) mass (kg) n ) number of batches per period P ) power (kW)
Ind. Eng. Chem. Res., Vol. 43, No. 24, 2004 7795 RR ) reflux ratio t ) time (s/period) T ) temperature (K) ∆H ) enthalpy change (kJ/kg) γ ) ratio of the actual power consumption of a motor to the nominal power η ) efficiency Indices A ) apparatus Air ) air Am ) ambient B ) barrel BC ) batch column CM ) chemical mass D ) distillate ES ) evaporated solvent El ) electricity HC ) heat chamber I ) infrastructure i ) chemical types (different PSPs) j ) apparatus type k ) number of different chemicals in a process step L ) loss m ) energy form N ) nominal n ) number of different aspects of an apparatus P ) production q ) indicator for different process steps/unit operations of one recipe (PSP) R ) reaction RM ) reaction mass RV ) reaction vessel S ) solvent St ) steam V ) vaporization W ) water
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Received for review April 30, 2004 Revised manuscript received September 2, 2004 Accepted September 13, 2004 IE049641J
