advertisement

3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics Energy Balance Calculation Related terms: Reactor, Biomass, Heat losses, Temperature, Cooling Water, Heat Transfer Coeﬃcient, Cooling Requirement, Fermenter, Heat Capacity, Steam Table View full index Energy Balances Pauline M. Doran, in Bioprocess Engineering Principles (Second Edition), 2013 5.2 General Energy Balance Equations The principle underlying all energy balance calculations is the law of conservation of energy, which states that energy can be neither created nor destroyed. Although this law does not apply to nuclear reactions, conservation of energy remains a valid principle for bioprocesses because nuclear rearrangements are not involved. In the followings, we will derive the equations used for solution of energy balance problems. The law of conservation of energy can be written as: {energyinthroughsystemboundaries}−{energyoutthroughsystemboundaries}={energyaccumulatedwithinthesystem} (5.4) For practical application of this equation, consider the system depicted in Figure 5.1. Mass Mi enters the system while mass Mo leaves. Both masses have energy associated with them in the form of internal, kinetic, and potential energies; ﬂow work is also being done. Energy leaves the system as heat Q; shaft work Ws is performed on the system by the surroundings. We will assume that the system is homogeneous without charge or surface energy eﬀects. Sign in to download full-size image https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 1/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics Figure 5.1. Flow system for energy balance calculations. To apply Eq. (5.4), we must identify the forms of energy involved in each term of the expression. If we group together the extensive properties and express them as speciﬁc variables multiplied by mass, Eq. (5.4) can be written: Mi(u+ek+ep+pv)i−Mo(u+ek+ep+pv)o−Q+Ws=ΔE (5.5) where subscripts i and o refer to inlet and outlet conditions, respectively, and ΔE represents the total change or accumulation of energy in the system. u is speciﬁc internal energy, ek is speciﬁc kinetic energy, ep is speciﬁc potential energy, p is pressure, and v is speciﬁc volume. All energies associated with the masses crossing the system boundary are added together; the energy transfer terms Q and Ws are considered separately. Shaft work appears explicitly in Eq. (5.5) as Ws; ﬂow work done by the inlet and outlet streams is represented as pv multiplied by mass. The energy ﬂows represented by Q and Ws can be directed either into or out of the system; appropriate signs must be used to indicate the direction of ﬂow. Because it is usual in bioprocesses that shaft work be done on the system by external sources, in this text we will adopt the convention that work is positive when energy ﬂows from the surroundings to the system as shown in Figure 5.1. Conversely, work will be considered negative when the system supplies work energy to the surroundings. On the other hand, we will regard heat as positive when the surroundings receive energy from the system— that is, when the temperature of the system is higher than that of the surroundings. Therefore, when Ws and Q are positive quantities, Ws makes a positive contribution to the energy content of the system while Q causes a reduction. These eﬀects are accounted for in Eq. (5.5) by the signs preceding Q and Ws. The opposite sign convention is sometimes used in thermodynamics texts; however, the choice of sign convention is arbitrary if used consistently. Equation (5.5) refers to a process with only one input and one output stream. A more general equation is Eq. (5.6), which can be used for any number of separate material ﬂows: ∑inputstreamsM(u+ek+ep+pv)−∑outputstreamsM(u+ek+ep+pv)−Q+Ws=ΔE (5.6) The symbol ∑ means summation; the internal, kinetic, potential, and ﬂow work energies associated with all output streams are added together and subtracted from the sum for all input streams. Equation (5.6) is a basic form of the ﬁrst law of thermodynamics, a simple mathematical expression of the law of conservation of energy. The equation can be shortened by substituting enthalpy h for u+pv as deﬁned by Eq. (5.3): ∑inputstreamsM(h+ek+ep)−∑outputstreamsM(h+ek+ep)−Q+Ws=ΔE (5.7) 5.2.1 Special Cases Equation (5.7) can be simpliﬁed considerably if the following assumptions are made: • Kinetic energy is negligible • Potential energy is negligible These assumptions are acceptable for bioprocesses, in which high-velocity motion and large changes in height or electromagnetic ﬁeld do not generally occur. Thus, the energy balance equation becomes: ∑inputstreams(Mh)−∑outputstreams(Mh)−Q+Ws=ΔE (5.8) Equation (5.8) can be simpliﬁed further in the following special cases: • Steady-state ﬂow process. At steady state, all properties of the system are invariant. Therefore, there can be no accumulation or change in the energy of the system: ΔE=0. The steady-state energy balance equation is: ∑inputstreams(Mh)−∑outputstreams(Mh)−Q+Ws=0 (5.9) Equation (5.9) can also be applied over the entire duration of batch and fed-batch processes if there is no energy accumulation; ‘output streams’ in this case refers to the harvesting of all mass in the system at the end of the process. Equation (5.9) is used frequently in bioprocess energy balances. https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 2/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics • Adiabatic process. A process in which no heat is transferred to or from the system is termed adiabatic; if the system has an adiabatic wall, it cannot release or receive heat to or from the surroundings. Under these conditions Q=0 and Eq. (5.8) becomes: ∑inputstreams(Mh)−∑outputstreams(Mh)+Ws=ΔE (5.10) Equations (5.8), (5.9), and (5.10) are energy balance equations that allow us to predict, for example, how much heat must be removed from a fermenter to maintain optimum conditions, or the eﬀect of evaporation on cooling requirements. To apply the equations we must know the speciﬁc enthalpy h of ﬂow streams entering or leaving the system. Methods for calculating enthalpy are outlined in the followings. Characteristics and Analysis of the Dynamic Operation of Steel Manufacturing Process Ruiyu Yin, in Theory and Methods of Metallurgical Process Integration, 2016 4.5.2 Research Method and Feature of Energy Flow in the Process To research energy ﬂow in the steel manufacturing process, instead of static mass balance and energy balance calculation, the dynamic operation model with input/output and ﬂuctuations should be proposed. By analogy to mass ﬂow study, the concepts of “ﬂow,” “running program,” and “process network” for energy ﬂow ought to be established to study its input and output and the conversion in the open, irreversible, and far-from-equilibrium process. It is necessary to change the massenergy balance calculation of an isolated spot into the energy ﬂow study in the network of dynamic operation of the entire manufacturing process (Yin, 2010b). Studies on the input and output character of “ﬂow” involve not only the nodes, connectors, and the space arrangement of the nodes and connectors, but also the dynamic operation program, especially the time-characteristic order. The concept of input and output of energy ﬂow involves not only the amount of energy but also the grade of energy and time-space factor as well as the operation program of energy ﬂow. This is helpful for constructing a rational energy network containing secondary energy utilization. This is also conducive to further improvement of the energy eﬃciency. 4.5.2.1 Input and Output Characteristics and Fluctuation Phenomena in Energy Flow of Steel Plant There are diﬀerent types of energy carrier and input-output expressions of energy parameters including the type of energy kind (gas, steam, self-generated power, and sensible heat), the grade of energy (gas’s kind and caloriﬁc value, steam’s temperature and pressure, mass ﬂow’s temperature), and the amount of energy at procedure nodes in plants, in diﬀerent spatiotemporal boundaries of the manufacturing process. These energy parameters at diﬀerent nodes in energy ﬂow possess ﬂuctuation phenomena also. So, it is diﬃcult to describe the dynamic operation by static state separately. It is necessary to establish the rational energy network, which will play to the comprehensive potential of the energy medium including primary energy and secondary energy to achieve the high eﬃciency and low cost of the entire process. The establishment of an energy ﬂow network, the rational energy recycling scheme with diﬀerent interaction coupling mechanisms, can be achieved by combining diﬀerent amounts of gas, steam, sensible heat of matters, potential heat, etc. within a generating spot and time. 4.5.2.2 The Limitations of Static Mass and Energy Balance Calculation for a Single Procedure During the study on the dynamic operation of the entire steel manufacturing process, it is diﬃcult to accurately evaluate the utilization and proﬁt of energy of the entire process by material and heat balance (mass-energy balance) of a single procedure. When calculating static material and heat balance of the single procedure, the result can only be used for one procedure or reactor. In the result, the kind of utilization of the secondary energy is absent, which will aﬀect the energy conversion eﬃciency. For example, when using BOF gas as a fuel, the eﬀect of its utilization is the thermal eﬃciency and can be simply calculated. It is also aﬀected by the types of reheating furnace and burner. However, when using BOF gas for https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 3/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics the production of quality lime, there are some additional advantages like high activity of lime because of ash-free fuel, and low sulfur in lime to increase desulfurization eﬃciency, hence, the lime consumption and temperature drop of the hot metal will be lowered. As another example, if a gas/steam pipe network of an enterprise appears to be defective or be in an abnormal state, the emission of gas and steam cannot be avoided. This type of energy loss can hardly be reﬂected in the material and heat balance of the single procedure. Therefore, the study of energy ﬂow in the steel manufacturing process should be performed by dynamic modeling of the input/output to the open system. The dynamic modeling should be developed on the fundamentals of “ﬂow,” “running program,” and “process networks.” STEADY-STATE LUMPED SYSTEMS W. Fred Ramirez, in Computational Methods in Process Simulation (Second Edition), 1997 2.2.3.3 Sparse Matrices MATLAB has extensive sparse matrix facilities. Sparse matrices arise in many engineering problems including those of material and energy balance calculations as illustrated earlier. It is more eﬃcient to deﬁne matrices as sparse when appropriate. If you are not sure if a matrix is sparse, then you can use the function issparse (A) which returns a value of 1 if A is sparse and 0 if it is not sparse. To deﬁne a matrix as sparse, we use the function sparse (rowpos, colpos, val, m,n)where rowpos are the positions of the nonzero row elements, colpos are the positions of the non–zero column elements, val are the values of the non–zero elements, m is the number of rows, and n the number of columns. For example, if Sign in to download full-size image The result is Sign in to download full-size image It is important to note that matrix operations such as *, +, −, \ produce sparse results if both operands are sparse. You can convert a full matrix to a sparse one by using the command b=sparse(a); https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 4/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics and a sparse matrix to a full one by the command a=full(b); There is signiﬁcant eﬃciency in properly using sparse matrix operations when appropriate. Review of System Design and Sizing Tools Santiago Silvestre, in Practical Handbook of Photovoltaics (Second Edition), 2012 4.1 Sizing Tools Sizing tools allow photovoltaic systems to be dimensioned, taking into account energy requirements, site location, and system costs (Table 3). Most of these software tools are relatively simple and help users automatically solve energy-balance calculations considering diﬀerent combination of PV system components, including batteries, modules, and loads. These tools are usually implemented using spreadsheets at diﬀerent level of complexity and oﬀer a ﬁrst approach for the evaluation of speciﬁc PV system applications. In their basic form, these tools are easy to use, and some solar cell manufacturers and vendors of PV system components oﬀer this kind of tool to potential customers (often via the Internet) to enable customers to adapt PV system components to their products. Similar sizing tools are also available that focus on speciﬁc studies for complete sets of modules, batteries, inverters, electronic power conditioning components, and loads from the principal manufacturers in the photovoltaic market. More sophisticated sizing tools are also available in the market, some oﬀering the possibility of optimising the size of each PV system component. More detailed analysis can then be carried out of the energy ﬂows in the PV system and the determination of critical periods along the year. Of particular interest is often the deﬁcit of energy associated with these periods of time and minimising the ﬁnal cost of the system for a speciﬁc application. Heat Transfer Pauline M. Doran, in Bioprocess Engineering Principles (Second Edition), 2013 9.6 Application of the Design Equations The equations in Sections 9.4 and 9.5 provide the essential elements for design of heat transfer systems. Figure 9.17 summarises the relationships involved. Equation (9.19) is used as the design equation; Qˆ and the inlet and outlet temperatures are available from energy balance calculations as described in Section 9.5.3. The overall heat transfer coeﬃcient is evaluated from correlations such as those given in Section 9.5.1; additional terms are included if the heat transfer surfaces are fouled. The temperature-diﬀerence driving force is estimated from the fermentation and cooling water temperatures. With these parameters at hand, the required heat transfer area can be determined. https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 5/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics Sign in to download full-size image Figure 9.17. Summary of relationships and equations for design of a fermenter cooling coil. Because metabolic rates, and therefore rates of heat production, vary during fermentation, the rate at which heat is removed from the bioreactor must also be varied to maintain constant temperature. Heat transfer design is based on the maximum heat load for the system. When the rate of heat generation drops, operating conditions can be changed to reduce the rate of heat removal. The simplest way to achieve this is to decrease the cooling water ﬂow rate. The procedures outlined in this chapter represent the simplest and most direct approach to heat transfer design. If several independent variables remain unﬁxed prior to the design calculations, many diﬀerent design outcomes are possible. When variables such as the type of ﬂuid, the mass ﬂow rates, and the terminal temperatures are unspeciﬁed, they can be manipulated to produce an optimum design, for example, the design yielding the lowest total cost per year of operation. Computer packages that optimise heat exchanger design are available commercially. 9.6.1 Heat Exchanger Design for Fermentation Systems Calculating the equipment requirements for heating or cooling of fermenters can sometimes be simpliﬁed by considering the relative importance of each heat transfer resistance. • For large fermentation vessels containing cooling coils, the ﬂuid velocity in the vessel is generally much slower than in the coils; accordingly, the tube-side thermal boundary layer is relatively thin and most of the heat transfer resistance is located on the fermenter side. Especially when there is no fouling in the tubes, the heat transfer coeﬃcient for the cooling water can often be omitted when calculating U. • Likewise, the pipe wall resistance can sometimes be ignored as conduction of heat through metal is generally very rapid. An exception is stainless steel, which is used widely in the fermentation industry because it does not corrode in mild acid environments, withstands repeated exposure to clean steam, and does not have a toxic eﬀect on cells. The low thermal conductivity of this material means that wall resistance may be important unless the pipe wall is very thin. https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 6/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics The correlations used to estimate fermenter-side heat transfer coeﬃcients, such as Eqs. (9.36) and (9.37), were not developed for fermentation systems and must not be considered to give exact values. Most of the available correlations were developed using small-scale equipment; little information is available for industrial-size reactors. As described in Section 8.13, aerobic fermentations are typically carried out in tall vessels using multiple impellers; however, there have been relatively few studies of heat transfer using this vessel conﬁguration. For helical cooling coils, geometric parameters such as the vertical separation between individual coils and the space between the coil and tank wall have been found to aﬀect heat transfer, particularly for viscous ﬂuids. Yet these factors are not included in most commonly used heat transfer correlations. Equations (9.36) and (9.37) were developed for ungassed systems but are applied routinely for aerobic fermentations. Gassing alters the value of fermenter-side heat transfer coeﬃcients, but the magnitude of the eﬀect and whether gassing causes an increase or decrease in heat transfer cannot yet be predicted. The rate of heat transfer in gas–liquid systems appears to depend on the distribution of bubbles in the vessel [7]; for example, accumulation of bubbles on or around the heat transfer surface is deleterious. Taking all of the above factors into account, it is apparent that correlations such as Eqs. (9.36) and (9.37) can provide only a starting point or rough estimate for evaluation of heat transfer coeﬃcients in fermenters. When estimating heat transfer coeﬃcients for non-Newtonian broths, the apparent viscosity (Section 7.5) can be substituted for μb in the correlation equations and dimensionless groups. However, this substitution is not straightforward when rheological parameters such as the ﬂow behaviour index n, the consistency index K, and the yield stress τ0 change during the culture. The apparent viscosity also depends on the shear rate in the fermenter, which varies greatly throughout the vessel. These factors make evaluation of heat transfer coeﬃcients for non-Newtonian systems diﬃcult. Application of the heat exchanger design equations to specify a fermenter cooling system is illustrated in Example 9.6. Example 9.6 Cooling-Coil Length in Fermenter Design A fermenter used for antibiotic production must be kept at 35°C. After considering the oxygen demand of the organism and the heat dissipation from the stirrer, the maximum heat transfer rate required is estimated as 550 kW. Cooling water is available at 10°C; the exit temperature of the cooling water is calculated using an energy balance as 25°C. The heat transfer coeﬃcient for the fermentation broth is estimated from Eq. (9.36) as 2150 W m−2 °C−1. The heat transfer coeﬃcient for the cooling water is calculated as 14 kW m−2 °C−1. It is proposed to install a helical cooling coil inside the fermenter; the outer diameter of the pipe is 8 cm, the pipe wall thickness is 5 mm, and the thermal conductivity of the steel is 60 W m−1 °C−1. An average internal fouling factor of 8500 W m−2 °C−1 is expected; the fermenter-side surface of the coil is kept relatively clean. What length of cooling coil is required? Solution Qˆ=550×103 W. As the temperature in the fermenter is constant, ΔT is calculated from Eq. (9.39): ΔT=(25−10)°Cln(35−1035−25)=16.4°C U is calculated using Eq. (9.25) after omitting hfh, as there is no fouling layer on the hot side of the coil: 1U=(12150Wm−2°C−1+5×10−3m60Wm−1°C−1+114×103Wm−2°C−1+18500Wm−2°C−1)= (4.65×10−4+8.33×10−5+7.14×10−5+1.18×10−4)m2°CW−1=7.38×10−4m2°CW−1U=1355Wm−2°C−1 Note the relative magnitudes of the four contributions to U: the cooling water ﬁlm coeﬃcient and the wall resistance make comparatively minor contributions and can often be neglected in design calculations. We can now apply Eq. (9.19) to evaluate the required surface area A: A=QˆUΔT=550×103W1355Wm−2°C−1(16.4°C)=24.75m2 https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 7/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics Equation (9.23) for the area of a cylinder can be used to evaluate the pipe length, L. As we have information for both the outer pipe diameter (=8 cm) and the inner pipe diameter (=outer pipe diameter−2×wall thickness=8 cm−2×5 mm=7 cm), we can use an average pipe radius to determine L: L=A2πR=24.75m22π(0.5(8×10−2+7×10−2)2)m=105.0m The length of coil required is 105 m. The cost of such a length of pipe is a signiﬁcant factor in the overall cost of the fermenter. Mixing and heat transfer are not independent functions in bioreactors. The impeller size and stirrer speed aﬀect the value of the heat transfer coeﬃcient in the fermentation ﬂuid; increased turbulence in the reactor decreases the thickness of the thermal boundary layer and facilitates rapid heat transfer. However, stirring also generates heat that must be removed from the reactor to maintain constant temperature. Although this energy contribution may be small in low-viscosity ﬂuids because of the dominance of the heat of reaction (Section 5.10), heat removal can be a severe problem in bioreactors containing highly viscous ﬂuids. Turbulent ﬂow and high heat transfer coeﬃcients are diﬃcult to achieve in viscous liquids without enormous power input, which itself generates an extra heat load. The eﬀect of gas sparging on the heat load is usually neglected for stirred bioreactors. The energy associated with sparging can be evaluated as described in Section 8.6; however, the contribution to overall cooling requirements is minor. Internal cooling coils typically add 15 to 25% to the cost of fermentation vessels. The length of coil that can be used is limited by the size of the tank. Because the temperature of fermentations must be maintained within a very narrow range, if the culture generates a large heat load, providing suﬃcient cooling can be a challenge. As discussed further in Section 9.6.2, heat transfer can be the limiting factor in fermentations. This is of particular concern for high-density cultures growing on carbon substrates with a high degree of reduction (Section 5.9.5), as large amounts of energy are released. Elevated broth viscosities compound the problem by dampening turbulence and preventing good mixing, thus reducing the rate of heat transfer. Because biological reactions take place at near-ambient temperatures, the temperature of the cooling water is always close to that of the fermentation; therefore, a relatively small driving force for heat transfer or ΔT value is inevitable. Improving the situation by refrigerating the cooling water involves a substantial increase in capital and operating costs and is generally not economically feasible. It may be possible to raise the cooling water ﬂow rate, but this is also limited by equipment size and cost considerations. Although using an external heat exchanger such as that illustrated in Figure 9.1(e) is an option for increasing the heat transfer capacity of fermenters, as described in Section 9.1.1 this introduces extra contamination risks and also adds signiﬁcantly to the overall cost of the equipment. In some cases, the only feasible heat transfer solution for fermentations with high heat loads is to slow down the rate of fermentation, thus slowing down the rate of heat generation. This might be achieved, for example, by reducing the cell density, decreasing the concentration of rate-limiting nutrients in the medium, or using a diﬀerent carbon source. 9.6.2 Relationship between Heat Transfer and Cell Concentration The design equation, Eq. (9.19), and the steady-state energy balance equation, Eq. (9.48), allow us to derive some important relationships for fermenter operation. Because cell metabolism is usually the largest source of heat in fermenters, the capacity of the system for heat removal can be linked directly to the maximum cell concentration in the reactor. Assuming that the heat dissipated from the stirrer and the cooling eﬀects of evaporation are negligible compared with the heat of reaction, Eq. (9.48) becomes: Qˆ=−ΔHˆrxn (9.49) In aerobic fermentations, the heat of reaction is related to the rate of oxygen consumption by the cells. As outlined in Section 5.9.2, approximately 460 kJ of heat is released for each gmol of oxygen consumed. Therefore, if QO is the rate of oxygen uptake per unit volume in the fermenter: ΔHˆrxn=(−460kJgmol−1)QOV https://www.sciencedirect.com/topics/engineering/energy-balance-calculation (9.50) 8/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics where V is the reactor volume. Typical units for QO are gmol m−3 s−1. ΔHˆrxn in Eq. (9.50) is negative because the reaction is exothermic. Substituting this equation into Eq. (9.49) gives: Qˆ=(460kJgmol−1)QOV (9.51) If qO is the speciﬁc oxygen uptake rate, or the rate of oxygen consumption per cell, QO=qOx, where x is the cell concentration. Typical units for qO are gmol g−1 s−1. Therefore: Qˆ=(460kJgmol−1)qOxV (9.52) Substituting this into Eq. (9.19) gives: (460kJgmol−1)qOxV=UAΔT (9.53) The fastest rate of heat transfer occurs when the temperature diﬀerence between the fermenter contents and the cooling water is at its maximum. This occurs when the cooling water is at its inlet temperature, that is, ΔT=(TF−Tci) where TF is the fermentation temperature and Tci is the water inlet temperature. Therefore, assuming that the cooling water remains at temperature Tci, we can derive from Eq. (9.53) an equation for the hypothetical maximum possible cell concentration supported by the heat transfer system: xmax=UA(TF−Tci)(460kJgmol−1)qOV (9.54) It is undesirable for the biomass concentration in fermenters to be limited by the heat transfer capacity of the cooling system. Ideally, the extent of growth should be limited by other factors, such as the amount of substrate provided. Therefore, if the maximum cell concentration estimated using Eq. (9.54) is lower than that desired from the process, the heat transfer facilities must be improved. For example, the area A could be increased by installing a longer cooling coil, or the overall heat transfer coeﬃcient could be improved by increasing the level of turbulence. Equation (9.54) was derived for fermenters in which shaft work could be ignored; if the stirrer adds signiﬁcantly to the total heat load, xmax will be smaller than that estimated using Eq. (9.54). Natural Gas Conversion VI Harry Audus, ... Samuel S. Tam, in Studies in Surface Science and Catalysis, 2001 4 Sasol-Type GTL Plant Design With and Without CO2 Abatement A conceptual plant design for a GTL plant based on Sasol-type technology was developed as the base case which was compared with a CO2 abatement case. Figure 1 summarizes the mass, energy, and carbon balance of the base case. Key parameters and assumptions employed in the process simulation model are summarized in Table 2. https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 9/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics Sign in to download full-size image Figure 1. Mass, Energy, & Carbon Balance Summary – Sasol-Type Design - Base Case Table 2. Key Parameters and Assumptions of the Sasol-Type GTL Plant Base Case Air Separation Unit Conventional, single train cryogenic air separation plant - Oxygen purity 99.5 mol% O2 Syngas Generation Oxygen-blown, autothermal natural gas reforming - Feed ratios: - H2O:C, mole/mole 0.65 - CO2:C, mole/mole 0.10 - O2:C, mole/mole 0.56 - Exit conditions: Pressure: 28 bar - H2:CO mole ratio 2.04 Hydrogen Separation Pressure swing adsorption - H2 purity > 99.5 mol% H2 F-T Synthesis Single SBCR reactor, cobalt catalyst in F-T synthesis derived liquid, internal heat recovery (steam raising), recycle of part of purge gas to syngas generation, catalyst makeup/activation, catalyst recovery and recycle https://www.sciencedirect.com/topics/engineering/energy-balance-calculation Temperature: 1014 °C 10/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics - Operating conditions Pressure: 26 bar Temperature: 220 °C - Anderson-Schulz-Flory Several values used to ﬁt slope of carbon-number distribution for cobalt catalyst distribution parameter (α) - CO conversion per pass 76% - Steam raising Saturated - 13 bar, 191 °C Product Upgrading Mild hydrocracking of ASTM-D86 350 + °C product (wax) - Operating conditions Pressure: 115 bar - Reactor LHSV 2 hr− 1 Temperature: 370 °C The capital cost estimate was based on a factored estimating technique. The ISBL equipment was sized and materials-ofconstruction selected based on the particular process conﬁguration, heat and energy balance calculations, and the conditions of the locally available utility streams. Additional ﬁeld costs, bulk materials, direct labor, indirect costs, etc., were developed based on cost factors mentioned above. Other ﬁeld costs, such as sales tax, freight costs, duties, etc., were site speciﬁc and developed separately as percentages of the total ﬁeld cost. The oﬀsite cost estimate was developed from Bechtel in-house data for similar size and type plants in the same site location. The IEA Financial Assessment Criteria was used to develop the costs for home oﬃce, fees, and services and plant contingency. The CO2 capture and compression area consisted of three plants: (1) feed gas hydrogenation and high-temperature CO shift, (2) MDEA-based CO2 removal, and (3) CO2 compression. The intent of this design was capture, prior to emission, the non-product carbon as a single species— CO2 in this instance—and to deliver it in a pure form and at high pressure to the plant battery limit. This study does not address the collection, transportation, and ultimate disposal/ sequestration of this CO2 stream. The purge gas from the F-T synthesis section was hydrogenated to remove the trace amounts of oxygenates and the remaining CO is converted to CO2 and hydrogen in the shift unit. A 30 wt% aqueous solution of mono-diethanolamine (MDEA) was used to remove CO2 from the shift reactor eﬄuent gas. The CO2 from the MDEA regenerator was recovered and compressed to 110 bar for pipeline delivery. The Test Cell as a Thermodynamic System A.J. Martyr, M.A. Plint, in Engine Testing (Fourth Edition), 2012 The Energy Balance of the Engine Table 3.3 shows a possible energy balance sheet for a cell in which a gasoline engine is developing a steady power output of 100 kW. TABLE 3.3. Simpliﬁed Energy Flows for a Test Cell Fitted with a Hydraulic Dynamometer and 100 kW Gasoline Engine Energy Balance In https://www.sciencedirect.com/topics/engineering/energy-balance-calculation Out 11/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics Energy Balance In Out Fuel 300 kW Exhaust gas 60 kW Ventilating fan power 5 kW Engine cooling water 90 kW Dynamometer cooling water 95 kW Ventilation air 70 kW Heat loss, walls and ceiling 15 kW Electricity for cell services 25 kW 330 kW 330 kW The energy balance for the engine is as follows: In Out Fuel 300 kW Power 100 kW Exhaust gas 90 kW Engine cooling water 90 kW Convection and radiation 20 kW 300 kW 300 kW Note that where ﬂuids (air, water, exhaust) are concerned the energy content is referred to an arbitrary zero, the choice of which is unimportant: we are only interested in the diﬀerence between the various energy ﬂows into and out of the cell. Given suﬃcient detailed information on a ﬁxed engine/cell system, it is possible to carry out a very detailed energy balance calculation (see Chapter 6 for a more detailed treatment). Alternatively, there are some commonly used “rule-of thumb” calculations available to the cell designer; the most common of these relates to the energy balance of the engine, which is known as the “30–30–30–10 rule”. This refers to the energy balance given in Table 3.4. TABLE 3.4. Example of the 30–30–30–10 Rule In Via Out Via Fuel 300 kW Dynamometer 30% (90+ kW) Exhaust system 30% (90 kW) Engine ﬂuids 30% (90 kW) Convection and radiation 10% (30 kW) The key lesson to be learnt by the nonspecialist reader is that: any engine test cell has to be designed to deal with energy ﬂows that are at least three times greater than the “headline” engine rating. To many, this will sound obvious but a common ﬁxation on engine power and a casual familiarity with, but lack of appreciation of, the energy density of petroleum fuels still lead people to signiﬁcantly underrate cell cooling systems. Like any rule of thumb this is crude, but it does provide a starting point for the calculation of a full energy balance and a datum from which we can evaluate signiﬁcant diﬀerences in balance caused by the engine itself and its mounting within the https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 12/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics cell. First, there are diﬀerences inherent in the engine design. Diesels will tend to transfer less energy into the cell than petrol engines of equal physical size. For example, testers of rebuilt bus engines, which have both vertical and horizontal conﬁgurations, often notice that diﬀerent models of diesels with the same nominal power output will show quite diﬀerent distribution of heat into the test cell air and cooling water. Second, there are diﬀerences in engine rigging in the cell that will vary the temperature and surface area of engine ancillaries such as exhaust pipes. Finally, there is the amount and type of equipment within the test cell, all of which makes a contribution to the convection and radiation heat load to be handled by the ventilation system. Specialist designers have developed their own versions of a test cell software model, based both on empirical data and theoretical calculation, all of which is used within this book. The version developed by colleagues of the author produces the type of energy balance shown in Figure 3.2. Table 3.5 lists just a selection, from an actual project, of the known data and calculated energy ﬂows that such programs have to contain in order to produce Figure 3.2. https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 13/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics Sign in to download full-size image FIGURE 3.2. Output diagram from a test cell thermal analysis and energy ﬂow program. TABLE 3.5. A Selection of the Known, Estimated, and Calculated Data, Based on a Known Engine, Required to Produce a Complete Energy and Fluid Flow Diagram as Shown in Figure 3.2 Engine and Fuel Data Power output 180 kW Engine max. power 180 kW Fuel Diesel Primary energy from fuel 468 kW Caloriﬁc value of fuel 43,000 kJ/kg Electricity Output https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 14/18 3/3/2019 Density of fuel Energy Balance Calculation - an overview | ScienceDirect Topics 0.830 kg/liter Combustion Air From AC dyno. 171 kW Cooling Water Loads Temp. intake 23 °C Lube oil HX (N/A) 0 kW Temp. after compressor 185 °C Engine jacket HX 98 kW Temp. after intercooler 55 °C Intercooler HX 29 kW Combustion air temp. 70 °C Chilled Water Exhaust Gas Fuel cooling 1 kW Manifold temp. 650 °C Cell Heat Loads (radiation from) Temp. after turbine 434 °C Engine block 13 kW Temp. in cell system 400 °C Exhaust in cell 9 kW Dilution air temp. 30 °C Cooling water 3 kW Temp. at cell exit 100 °C Dynamometer 9 kW Dilution air ratio 3 Exhaust System Plant Cooling Water Exhaust gas out of cell Glycol content (%) 50% Temperature in 22 °C Temperature out 32 °C Speciﬁc heat capacity 3.18 kJ/l K Density 1.06 kg/m3 Peak Fuel Consumption 135 kW Exhaust Dilution Mass rate 39.1 kg/h Ratio (kg air/kg exh.) 3 Volume rate 47.2 liter/h Mass ﬂow at intake (air) 2519 kg/h 0.79 liter/min Density at intake (−50 Pa) 1.16 kg/m3 0.217 kg/kWh Volume rate at intake 2164 m3/h Speciﬁc fuel consumption 0.60 m3/s Combustion Air Mass rate 800 kg/h Total mass ﬂow (air + exh.) 3358 kg/h Density at 1 bar abs. 1.19 kg/m3 Mixture temp. after intake 123 °C Volume rate 672 m3/h Mixture density out of cell 0.95 kg/m3 0.19 m3/s Mixture ﬂow out of cell 3551 m3/h 0.99 m3/s Exhaust Gas Mass rate (air + fuel) 840 kg/h Density, after turbine 0.739 kg/m3 Volume rate after turbine 1136 m3/h Density, out of cell 0.517 kg/m3 Volume rate out 1622 m3/h https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 15/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics of closed system 0.45 m3/s These types of models are a type of “expert system”, the result of reﬁnement based on experience of many man-years. Such tools are extremely useful but cannot be used uncritically as the ﬁnal basis of design when a range of engines need to be tested or the design has to cover two or more cells in a facility where ﬂuid services are shared; in those cases the energy diversity factor has to be considered. Heat Integration: Targets and Heat Exchanger Network Design Truls Gundersen, in Handbook of Process Integration (PI), 2013 4.3.2 Major Challenges in Data Extraction There are two very diﬀerent types of challenges related to data extraction for a Heat Integration project: (i) To establish the most correct set of data related to ﬂowrates and thermodynamic conditions of process streams used as input to heat recovery analysis and design. (ii) To represent the heating, cooling, evaporation and condensation needs of the process streams in such a way that the degrees of freedom are kept open for network design. While activity (i) is fairly straightforward (but involves a lot of work), activity (ii) requires skill and experience. It has often been stated that data extraction (in particular the second part) is more art than science, and most of the attempts to provide procedures and guidelines for this activity have failed, including the development of knowledge-based systems (also referred to as expert systems). Some of the commercial general-purpose process simulators have features for automatic stream data extraction on the basis of a converged steady-state mass and energy-balance calculation. While these procedures enable easy generation of Composite and Grand Composite Curves, they do not keep the degrees of freedom open. Despite the importance of data extraction, the topic has not been much discussed in the literature beyond textbooks on Process Integration, such as Linnhoﬀ et al. (1982), Smith (2005) and Kemp (2007). The topic is also thoroughly covered in the recent book by Klemeš et al. (2010). Interestingly, rather detailed literature on data extraction has been provided in the form of reports from research institutes (such as , CANMET 2003), software vendors (such as AspenTech, 2009) and consulting companies (such as Linnhoﬀ March, 1998), which again illustrates the importance of proper data extraction for a successful Heat Integration project. For manual data extraction, the following guidelines can be useful: (a) Do not copy all features of the conceptual ﬂowsheet or an existing design. (b) Do not mix streams at diﬀerent temperatures. (c) Do not include utilities as stream data. (d) Do not accept the prejudice of colleagues against Heat Integration. (e) Do not ignore true practical constraints. (f ) Distinguish between soft and hard stream data. Rule (a) refers to the issue of keeping the degrees of freedom open in order not to overlook promising solutions for heat recovery systems. Rule (b) involves several aspects, and should be discussed in more depth. First, a mixer can act as a heat exchanger, thus saving Capital Cost; however, mixing streams of diﬀerent compositions is only an option if the streams are entering the same unit operation, such as a chemical reactor. Second, mixing streams at diﬀerent temperatures introduces exergy losses and should be avoided. Third, mixing streams may eliminate potential heat recovery solutions. Finally, mixing https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 16/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics streams may be required from a practical point of view, such as adding steam to hydrocarbon streams to avoid coking inside pipes and equipment, or it may be forbidden from a safety point of view, such as mixing oxygen-rich streams with hydrocarbon streams. Rule (c) is rather obvious, since the goal of the exercise is to establish minimum utility requirements; however, there are cases where it is not so easy to distinguish whether a stream is a process stream or is acting as a utility. Rule (d) relates to the common reluctance in the process industries to accept Heat Integration solutions from an operational point of view; however, it is a fact that most industrial processes are heavily integrated, and rather than focusing on Maximum Heat Recovery, one should focus on correct or appropriate Heat Integration. In addition, it should be mentioned that when the economic potential of Heat Integration is established and well documented, it is often easier to get acceptance for such projects. Rule (e) means that, even though one should try to keep the degrees of freedom open, obviously one should not forget that some practical constraints cannot be ignored. One example is related to metal dusting, a severe form of corrosive degradation of metals that happens in some temperature ranges when CO is present. This is a problem in synthesis gas production, and in order to keep the metal temperature at a suﬃciently low level, the boiler is placed upstream of the steam superheater, which is not the best solution from a thermodynamic point of view as conveyed in Pinch Analysis. Finally, Rule (f ) is quite important in the sense that some stream data must be considered as hard speciﬁcations, while others can be adjusted if that improves or simpliﬁes the heat recovery system (as discussed in Chapter 2 on Basic Process Integration Terminology). An inlet temperature to a reactor or distillation column must often be regarded as a hard speciﬁcation, while the target temperature of a process stream going to some sort of storage is an example of soft process data. Specifying a low target temperature for a hot product stream going to storage in order to increase the heat recovery potential will only result in increased need for external cooling if the target temperature is below the Pinch Temperature. Instead, this cooling could have been taken care of by nature itself through convective heat loss to the environment. Returning to activity (i) of the data extraction exercise, there are two distinctly diﬀerent situations. For grassroots design, there is normally a simulation model available for the process providing stream data as part of a steady-state material and energy balance calculation. The advantage in this case is that the data are consistent. As an example, the hot and cold side of a heat exchanger will always be in balance for a converged simulation. The quality of the data, however, depends on to what extent the simulation models describes the behaviour of the real process. For retroﬁt design, in addition to using a simulation model if available, one could resort to the original speciﬁcation sheets for the process, or one could use measurements from the plant. However, the plant may have been modiﬁed several times since its start-up, and ﬂowsheets and speciﬁcation sheets are not always updated. Regarding the use of measurements, the typical situation is that some measurements are missing, and instruments may be either not functioning at all, or they may give incorrect readings. In such cases, the task of data reconciliation can be enormous, and a key to success is to work very closely with operators and plant engineers. About ScienceDirect Remote access Shopping cart Contact and support Terms and conditions Privacy policy We use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies. Copyright © 2019 Elsevier B.V. or its licensors or contributors. ScienceDirect ® is a registered trademark of Elsevier B.V. https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 17/18 3/3/2019 Energy Balance Calculation - an overview | ScienceDirect Topics https://www.sciencedirect.com/topics/engineering/energy-balance-calculation 18/18