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The heat capacity at constant pressure of a gas is determined experimentally at several temperatures, with the following results: $$\begin{array}{|l|c|c|c|c|c|c|c|} \hline T\left(^{\circ} \mathrm{C}\right) & 0 & 100 & 200 & 300 & 400 & 500 & 600 \\ \hline C_{p}\left[\mathrm{J} /\left(\mathrm{mol} \cdot^{\circ} \mathrm{C}\right)\right] & 33.5 & 35.1 & 36.7 & 38.4 & 40.2 & 42.0 & 43.9 \\ \hline \end{array}$$ (a) Calculate the heat (kW) required to raise 150 mol/s of the gas from 0^ C to 600^'C, using Simpson's rule (Appendix A.3) to integrate the tabulated heat capacities. (b) Use the method of least squares (Appendix A.1) to derive a linear expression for \(C_{p}(T)\) in the range \(0^{\circ} \mathrm{C}\) to \(600^{\circ} \mathrm{C},\) and use this expression to estimate once again the heat ( \(\mathrm{kW}\) ) required to raise 150 mol/s of the gas from 0 ^ C to 600^'C. If the estimates differ, in which one would you have more confidence, and why?

Short Answer

Expert verified
Without the exact calculations, these will be general steps to solve the problem. Start with the application of Simpson's rule to calculate the required heat then use method of least squares to develop a linear function for\(C_{p}\) and re-estimate the required heat. If the estimates differ, Simpson's rule should be trusted more because it considers the \(C_{p}\) to be a function of T, rather than a linear relation.

Step by step solution

01

Applying Simpson's rule

First, we need to calculate heat required using Simpson's rule. The yeild is represented by the integral of Cp over a certain range. Since we have an even number of segments, we can apply Simpson's rule. Simpson's Rule is given by \((b−a)/3n [y0+4(y1+y3+++yn−1)+2(y2+y4+..+yn−2)+yn]\). Here, \(a = 0, b = 600, n = 6\) and \(yi\) represents \(Cp\) values. After performing these calculations, we multiply by the number of moles (150 mol/s) to find the total heat absorbed.
02

Applying Method of Least Squares

Secondly, moving to the method of least squares to find coefficients a and b. We utilize the equations: \(na+b\sum{X}=\sum{Y}\) and \(\sum{X}a+b\sum{X^2}=\sum{XY}\), where X values are temperature and Y values represent \(C_p\). The sums are taken over i from 1 to 7. Once the coefficients of a and b are found, replace \(C_{p}\) with the expression \(a + bT\) in the integration, convert the heat capacity to appropriate units and then use the integral of the function to calculate the heat which is in \(kW\).
03

Comparison of the solutions

Finally, we compare the two obtained values. If they differ, it’s important to know that the Simpson's rule gives us a more accurate estimate because it considers the \(C_{p}\), to be a function of T. That's in contrast to the least squares approach which assumes a linear relation between T and \(C_{p}\). Therefore, if the estimates differ, more confidence should be on the value calculated using Simpson's rule.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Simpson's Rule
Simpson's rule is a method for numerical integration, the process of calculating the area under a curve. It's particularly handy when dealing with data points as opposed to continuous functions. To apply Simpson's rule, you divide the range of integration into an even number of segments, calculate the area of each segment, and then sum these areas to find the total.

The formula for Simpson's rule involves endpoints and midpoints of the segments, weighted accordingly. The end segments (the first and last data points) are not multiplied by any factor, the midpoint segments (odd-indexed data points) are multiplied by four, and the remaining segments (even-indexed data points, excluding the last) are multiplied by two. To finalize, you multiply by one-third of the segment width.

This approach is especially effective for smoothly varying functions but is also robust enough to handle functions that change rapidly, as it includes a higher-order polynomial estimation than simple trapezoidal methods. When students need to perform heat capacity calculations, Simpson's rule aids in integrating data points with varying heat capacities at different temperatures, providing a more precise estimate of the total heat required for a process.
Method of Least Squares
The method of least squares is a foundational statistical tool used to determine the best-fitting line through a set of data points. This method minimizes the sum of the squares of the residuals, the differences between the observed and estimated values. It's particularly useful in heat capacity calculations when you seek a simple linear relationship between temperature and heat capacity.

In mathematical terms, the method involves finding the coefficients of a linear equation, typically represented as y = a + bx, such that the sum of the squares of the vertical distances of the points from the line is minimized. This is done by solving a set of normal equations derived from taking partial derivatives of the sum of squares with respect to the coefficients and setting them to zero.

Applying the method of least squares in the context of temperature-dependent heat capacity can simplify complex relationships into a linear model, offering a useful approximation for initial estimates and simpler calculations. However, this method assumes homoscedasticity, meaning the variance around the regression line is the same for all values of the predictor variable.
Integrating Heat Capacities
Integrating heat capacities is a process often necessary in thermodynamics when determining the amount of heat required to change the temperature of a substance. The integral of heat capacity with respect to temperature provides the total heat absorbed or released during the temperature change.

For substances with temperature-independent heat capacities, this is a straightforward calculation. However, for a gas or any other substance with a temperature-dependent heat capacity, the integration becomes more complex. In such cases, numerical integration techniques, such as Simpson's rule, become crucial.

When integrating discrete heat capacity values over a temperature range, as in the given exercise, it’s vital to use a method that captures the nuance of the temperature-dependence. This means taking into account the variation of heat capacity values at different temperatures, which lead to more accurate calculations of the total heat compared to a simple linear approximation.
Temperature-Dependent Heat Capacity
Heat capacity is an intrinsic property of a substance that indicates the amount of heat required to change its temperature by a given amount. It's often denoted by the symbol Cp for constant pressure conditions. Heat capacity can vary with temperature, a characteristic especially pronounced in gases.

In such cases, heat capacity is represented as a function of temperature, commonly noted as Cp(T). Experimentally, this function can be determined by measuring heat capacity at various temperatures. When the heat capacity varies with temperature, a simple constant model for Cp is insufficient, and calculations for heat transfer must integrate the temperature-dependent heat capacity over the desired temperature range.

The given exercise demonstrates this concept by requiring the integration of heat capacities across a broad temperature interval. Understanding the variability of heat capacity with temperature is crucial in accurately gauging the energy involved in heating processes, making it an essential aspect of thermodynamics and the study of materials' behavior under different thermal conditions.

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Most popular questions from this chapter

In gas adsorption a vapor is transferred from a gas mixture to the surface of a solid. (See Section \(6.7 .\) ) An approximate but useful way of analyzing adsorption is to treat it simply as condensation of vapor on a solid surface. Suppose a nitrogen stream at \(35^{\circ} \mathrm{C}\) and 1 atm containing carbon tetrachloride with a \(15 \%\) relative saturation is fed at a rate of \(10.0 \mathrm{mol} / \mathrm{min}\) to a \(6-\mathrm{kg}\) bed of activated carbon. The temperature and pressure of the gas do not change appreciably from the inlet to the outlet of the bed, and there is no \(\mathrm{CCl}_{4}\) in the gas leaving the adsorber. The carbon can adsorb 40\% of its own mass of carbon tetrachloride before becoming saturated, at which point it must be either regenerated (remove the carbon tetrachloride) or replaced with a fresh bed of activated carbon. Neglect the effect of temperature on the heat of vaporization of \(\mathrm{CCl}_{4}\) when solving the following problems: (a) Estimate the rate ( \(\mathrm{kJ} / \mathrm{min}\) ) at which heat must be removed from the adsorber to keep the process isothermal, and the time (min) it will take to saturate the bed. (b) The surface-to-volume ratio of spherical particles is \((3 / r)\left(\mathrm{cm}^{2} \text { outer surface }\right) /\left(\mathrm{cm}^{3} \text { volume }\right)\) First, derive that formula. Second, use it to explain how decreasing the average diameter of the particles in the carbon bed might make the adsorption process more efficient. Third, since most of the area on which adsorption takes place is provided by pores penetrating the particle, explain why the surface-to-volume ratio, as calculated by the above expression, might be relatively unimportant.

A natural gas containing 95 mole \(\%\) methane and the balance ethane is burned with \(20.0 \%\) excess air. The stack gas, which contains no unburned hydrocarbons or carbon monoxide, leaves the furnace at \(900^{\circ} \mathrm{C}\) and \(1.2 \mathrm{atm}\) and passes through a heat exchanger. The air on its way to the furnace also passes through the heat exchanger, entering it at \(20^{\circ} \mathrm{C}\) and leaving it at \(245^{\circ} \mathrm{C}\). (a) Taking as a basis \(100 \mathrm{mol} / \mathrm{s}\) of the natural gas fed to the furnace, calculate the required molar flow rate of air, the molar flow rate and composition of the stack gas, the required rate of heat transfer in the preheater, \(\dot{Q}\) (write an energy balance on the air), and the temperature at which the stack gas leaves the preheater (write an energy balance on the stack gas). Note: The problem statement does not give you the fuel feed temperature. Make a reasonable assumption, and state why your final results should be nearly independent of what you assume. (b) What would \(\dot{Q}\) be if the actual feed rate of the natural gas were 350 SCMH [standard cubic meters per hour, \(\left.\mathrm{m}^{3}(\mathrm{STP}) / \mathrm{h}\right] ?\) Scale up the flowchart of Part (a) rather than repeating the entire calculation.

Among the best-known building blocks in nanotechnology applications are nanoparticles of noble metals. For example, colloidal suspensions of silver or gold nanoparticles (10-200 nm) exhibit vivid colors because of intense optical absorption in the visible spectrum, making them useful in colorimetric sensors. In the illustration shown below, a suspension of gold nanoparticles of a fairly uniform size in water exhibits peak absorption near a wavelength of \(525 \mathrm{nm}\) (near the blue region of the visible spectrum of light). When one views the solution in ambient (white) light, the solution appears wine-red because the blue part of the spectrum is largely absorbed. When the nanoparticles aggregate to form large particles, an optical absorption peak near \(600-700 \mathrm{nm}\) (near the red region of the visible spectrum) is observed. The breadth of the peak reflects a fairly broad particle size distribution. The solution appears bluish because the unabsorbed light reaching the eye is dominated by the short (blue-violet) wavelength region of the spectrum. since the optical properties of metallic nanoparticles are a strong function of their size, achieving a narrow particle size distribution is an important step in the development of nanoparticle applications. A promising way to do so is laser photolysis, in which a suspension of particles of several different sizes is irradiated with a high-intensity laser pulse. By carefully selecting the wavelength and energy of the pulse to match an absorption peak of one of the particle sizes (e.g., irradiating the red solution in the diagram with a \(525 \mathrm{nm}\) laser pulse), particles of or near that size can be selectively vaporized. (a) A spherical silver nanoparticle of diameter \(D\) at \(25^{\circ} \mathrm{C}\) is to be heated to its normal boiling point and vaporized with a pulsed laser. Considering the particle a closed system at constant pressure, write the energy balance for this process, look up the physical properties of silver that are required in the energy balance, and perform all the required substitutions and integrations to derive an expression for the energy \(Q_{\text {abs }}(\mathrm{J})\) that must be absorbed by the particle as a function of \(D(\mathrm{nm})\) (b) The total energy absorbed by a single particle \(\left(Q_{\text {abs }}\right)\) can also be calculated from the following relation: $$ Q_{\mathrm{abs}}=F A_{\mathrm{p}} \sigma_{\mathrm{abs}} $$ where \(F\left(\mathrm{J} / \mathrm{m}^{2}\right)\) is the energy in a single laser pulse per unit spot area (area of the laser beam) and \(A_{\mathrm{p}}\left(\mathrm{m}^{2}\right)\) is the total surface area of the nanoparticle. The effectiveness factor, \(\sigma_{\mathrm{ahs}},\) accounts for the efficiency of absorption by the nanoparticle at the wavelength of the laser pulse and is dependent on the particle size, shape, and material. For a spherical silver nanoparticle irradiated by a laser pulse with a peak wavelength of \(532 \mathrm{nm}\) and spot diameter of \(7 \mathrm{mm}\) with \(D\) ranging from 40 to \(200 \mathrm{nm}\), the following empirical equation can be used for \(\sigma_{\mathrm{abs}}\) $$ \sigma_{\mathrm{abs}}=\frac{1}{4}\left[0.05045+2.2876 \exp \left(-\left(\frac{D-137.6}{41.675}\right)^{2}\right)\right] $$ where \(\sigma_{\text {abs }}\) and the leading \(\frac{1}{4}\) are dimensionless and \(D\) has units of nm. Use the results of Part (a) to determine the minimum values of F required for complete vaporization of single nanoparticles with diameters of \(40.0 \mathrm{nm}, 80.0 \mathrm{nm},\) and \(120.0 \mathrm{nm}\). If the pulse frequency of the laser is \(10 \mathrm{Hz}\) (i.e., 10 pulses per second), what is the minimum laser power \(P(\mathrm{W})\) required for each of those values of D? (Hint: Set up a dimensional equation relating \(P\) to \(F\).) (c) Suppose you have a suspension of a mixture of \(D=40 \mathrm{nm}\) and \(D=120 \mathrm{nm}\) spherical silver nanoparticles and a \(10 \mathrm{Hz} / 532 \mathrm{nm}\) pulsed laser source with a \(7 \mathrm{nm}\) diameter spot and adjustable power. Describe how you would use the laser to produce a suspension of particles of only a single size and state what that size would be.

A mixture of \(n\) -hexane vapor and air leaves a solvent recovery unit and flows through a \(70-\mathrm{cm}\) diameter duct at a velocity of \(3.00 \mathrm{m} / \mathrm{s}\). At a sampling point in the duct the temperature is \(40^{\circ} \mathrm{C}\), the pressure is \(850 \mathrm{mm}\) Hg, and the dew point of the sampled gas is \(25^{\circ} \mathrm{C}\). The gas is fed to a condenser in which it is cooled at constant pressure, condensing \(70 \%\) of the hexane in the feed. (a) Perform a degree-of-freedom analysis to show that enough information is available to calculate the required condenser outlet temperature \(\left(^{\circ} \mathrm{C}\right)\) and cooling rate \((\mathrm{kW})\) (b) Perform the calculations. (c) If the feed duct diameter were \(35 \mathrm{cm}\) for the same molar flow rate of the feed gas, what would be the average gas velocity (volumetric flow rate divided by cross-sectional area)? (d) Suppose you wanted to increase the percentage condensation of hexane for the same feed stream. Which three condenser operating variables might you change, and in which direction?

Propane gas enters a continuous adiabatic heat exchanger \(^{17}\) at \(40^{\circ} \mathrm{C}\) and \(250 \mathrm{kPa}\) and exits at \(240^{\circ} \mathrm{C}\). Superheated steam at \(300^{\circ} \mathrm{C}\) and 5.0 bar enters the exchanger flowing countercurrently to the propane and exits as a saturated liquid at the same pressure. (a) Taking as a basis 100 mol of propane fed to the exchanger, draw and label a process flowchart. Include in your labeling the volume of propane fed \(\left(\mathrm{m}^{3}\right),\) the mass of steam fed \((\mathrm{kg}),\) and the volume of steam fed \(\left(\mathrm{m}^{3}\right)\) (b) Calculate values of the labeled specific enthalpies in the following inlet-outlet enthalpy table for this process. $$\begin{array}{|l|cc|cc|} \hline \text { Species } & n_{\text {in }} & \hat{H}_{\text {in }} & n_{\text {out }} & \hat{H}_{\text {out }} \\ \hline \mathrm{C}_{3} \mathrm{H}_{8} & 100 \mathrm{mol} & \hat{H}_{\mathrm{a}}(\mathrm{kJ} / \mathrm{mol}) & 100 \mathrm{mol} & \hat{H}_{\mathrm{c}}(\mathrm{kJ} / \mathrm{mol}) \\ \mathrm{H}_{2} \mathrm{O} & m_{\mathrm{w}}(\mathrm{kg}) & \hat{H}_{\mathrm{b}}(\mathrm{kJ} / \mathrm{kg}) & m_{\mathrm{w}}(\mathrm{kg}) & \hat{H}_{\mathrm{d}}(\mathrm{kJ} / \mathrm{kg}) \\ \hline \end{array}$$ (c) Use an energy balance to calculate the required mass feed rate of the steam. Then calculate the volumetric feed ratio of the two streams ( \(\mathrm{m}^{3}\) steam fed \(/ \mathrm{m}^{3}\) propane fed). Assume ideal-gas behavior for the propane but not the steam and recall that the exchanger is adiabatic. (d) Calculate the heat transferred from the water to the propane ( \(k J / m^{3}\) propane fed). (Hint: Do an energy balance on either the water or the propane rather than on the entire heat exchanger.) (e) Over a period of time, scale builds up on the heat-transfer surface, resulting in a lower rate of heat transfer between the propane and the steam. What changes in the outlet streams would you expect to see as a result of the decreased heat transfer?

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