Relative Deviation Error Research Articles

Comparative Mass Transfer Study of Basic and Acid Magenta Adsorption onto Natural Clay

In this work a comparative study of basic and acid magenta sorption on Algerian natural untreated clay was investigated using theoretical models for the following conditions C0(BM) = 200 mg/L, C0(AM) = 150 mg/L, V = 500 mL, CB(BM) = 1g/L, T = 22 °C. Adsorption mechanism of both dyes based on an intraparticle diffusion, external mass transfer, and kinetic models was examined. Statistical error functions regression coefficient (R2), the root mean square error (RMSE) and the average relative error deviation ARED were used to estimate the deviation between experimental and theoretical values. This work indicated that the experimental results obtained for both dyes fitted well the chosen models in the following order: External model of Boyd < Kinetic model < Urano and Tachikawa model < External model of Weber and Morris ≤ Weber and Morris internal diffusion model. However, the calculated values of Biot number are 32.31 and 69.33 for acid magenta and basic magenta respectively, indicating that the adsorption of both dyes onto the same clay is initially controlled by external film diffusion at the first ten minutes. The adsorption capacity of the tested clay for both dyes is remarkable compared to other natural adsorbents. Where the best results were obtained for basic magenta (qexp = 198.028 mg g–1, R2 = 0.992, ARED = 0.128 and RMSE = 0.461).

Errors in phase velocity estimation owing to the method used for shear wave waveform phase extraction

In ultrasound elastography, the shear wave phase velocity, cs, is often used to calculate the stiffness, which can indicate whether a biological tissue has an abnormality. The shear wave phase velocity can also be used to compute shear wave dispersion, which can be related to viscosity or guided wave effects. The most common methods used for extracting the phase from waveform vibration to estimate cs are the Kalman filter (KF), the fast Fourier Transform (FFT), and the Levenberg–Marquardt Algorithm (LMA). Currently, there are no studies in the literature comparing the performance of these methods; hence, this study aims to show the error in estimating the phase velocity obtained with the mentioned methods and to compare them through computational simulations and experiments. In the computational simulations, the absolute value of mean and the standard deviation of relative error of cs were evaluated as a function of signal-to-noise ratio, number of cycles of the shear wave, pulse repetition frequency, and initial phase. The experimental setup used an ultrasonic benchtop system, with a 2.104-MHz frequency transducer, TV, to generate acoustic radiation force at a plastic sphere and consequently induce shear wave propagation in a 7% gelatin phantom. Another ultrasonic benchtop system, with a 4.89-MHz frequency transducer, was used on pulse-echo mode to monitor the shear wave propagation at different positions within the phantom. The computational simulation results demonstrated that LMA and KF demonstrated the best performance in extracting the phase employed to estimate the shear wave phase velocity. In some situations, the cs values obtained by means of KF, FFT, and LMA methods in the experimental results presented a statistical difference, but the highest differences between the mean of cs was smaller than 0.03 m s−1.

Radial basis Function Neural Network for Predicting Flow Bottom Hole Pressure

The ability to monitor the flow bottom hole pressure in pumping oil wells provides important information regarding both reservoir and artificial lift performance. This paper proposes an iterative approach to optimize the spread constant and root mean square error goal of the radial basis function neural network. In addition, the optimized network is utilized to estimate this oil well pressure. Simulated experiments and qualitative comparisons with the most related techniques such as feedforward neural networks, neuro-fuzzy system, and the empirical model have been conducted. The achieved results show that the proposed technique gives better performance in estimating the flow of bottom hole pressure. Compared with the other developed techniques, an improvement of 7.14% in the root mean square error and 3.57% in the standard deviation of relative error has been achieved. Moreover, 90% and 95% accuracy of the proposed network are attained by 99.6% and 96.9% of test data, respectively.

Signal processing method and performance tests on weighting-sensor-based measuring system of output quantity for a seeding and fertilizing applicator

In terms of a weighting-sensor-based measuring system of output quantity for a seeding and fertilizing applicator, a signal processing method which could reliably obtain real-time output quantity was designed by collecting and analyzing the signals of the measuring system in the field. This method was consisted of several procedures as follow: low-pass filtering, mean filtering after high-frequency sampling, moving filtering and linear fitting. The low-pass filter was mainly used to eliminate the high-frequency vibration interference produced by the interaction between the rotary cultivator and the soil during field operation. The mean filtering after high-frequency sampling aimed at weakening the random vibration interference. The main function of moving filtering was to remove the low-frequency interference. And the linear fitting was applied in the initial and terminal stages of the sowing and fertilizing output quantity detection in order to obtain more reliable gross output quantity of sowing and fertilizing. In order to test the performance of the signal processing method, the field tests of sowing and fertilizing output quantity were carried out. During the tests, 3 levels of target sowing and fertilizing quantity (150Kg/ha, 300Kg/ha, 375Kg/ha for seeding, 225Kg/ha, 375Kg/ha, 525Kg/ha for fertilizing), and 2 levels of vehicle speeds (approximately 2Km/h and 3Km/h respectively ) were set to carry out multi-factor cross experiments. Each kind of test repeated 3 times and had a testing distance around 300m in the field. Field test results showed that the maximum relative error of cumulative output quantity was 4.88%, the average relative error was 2.62%, and the standard deviation of relative error was 1.7%. The linear correlation coefficient R2 between the measured output quantity and the actual output quantity was 0.998. It can be concluded that the designed signal processing method can reliably extract the effective signal of the weighing-sensor-based measuring system of output quantity, and it is expected to provide real-time feedback signal for the output quantity control system and improve the intelligent operation level of the seeding and fertilizing machine.

Measurement accuracy of the area of combine-harvested field sections corresponding to a truckload of grain received at a grain elevator

Measuring the area of combine-harvested field sections corresponding to a truckload of grain received at a grain elevator (LHA: load harvest area) allows the estimation of brown rice yield. The objective of this study was to evaluate the accuracy of LHA measured based on combine movement traces when the traces were recorded by a GPS or a GPS + Quasi-Zenith Satellite System (QZSS) receiver. For the measurements using a GPS receiver, the mean and standard deviation of relative errors of LHA were 4.7% and 8.1%. The center of the relative error distribution shifted toward positive because positioning errors of a GPS receiver shifted outer edges of harvested sections outward and resulted in measuring larger harvested area than a true value. For the measurements using a GPS + QZSS receiver, the mean and standard deviation of relative errors of LHA were 0.5% and 4.8%. The center of the relative error distribution was around 0% for its better positioning accuracy. The relative errors of LHA tended to be large when an LHA was measured from a field section where only the outer-part of a field is harvested or several small harvested sections. These cases of LHA measurements increased the sum of the perimeter of harvested sections and resulted in deteriorating the measurement accuracy. This result indicated that measurement accuracy of LHA can be further improved by correcting error, which can be estimated based on positioning errors and the perimeter of harvested sections.

Artificial Intelligent techniques for Flow Bottom Hole Pressure Prediction

This paper proposes Radial Basis and Feed-forward Neural Networks to predict the flowing bottom-hole pressure in vertical oil wells. The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and the standard statistical analysis has been accomplished on the achieved results to validate the models’ prediction accuracy. For the sake of qualitative comparison, empirical modes have been developed. The obtained results show that the proposed Feed-Forward Neural Network models outperforms and capable of estimating the FBHPaccurately.The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 375 sets only to give a 3.4 RMES and 97% of the test data achieved 90% accuracy.

Comparison of Non-Homogeneous Poisson Process Software Reliability Models in Web Applications

Software reliability is an important quality factor that effects project success. By modelling software reliability, it can be estimated when and with how much effort a project can be deployed. Consequently, this can contribute to the resource and schedule planning of a project. Therefore, software reliability models SRM are frequently used for measuring the maturity of a software. A number of studies exist in the literature that compare SRMs in terms of their modelling performance. However, there is a need of evaluating these SRMs by taking into account the software project domain. This study aims to compare the performance of SRMs in the context of Web applications. In accordance to this purpose, six different software reliability models, namely Goel-Okumoto, Musa Exponential, Inflected S-shaped, Delayed S-shaped, Yamada and Pham Nordmann Zhang Imperfect Fault Detection PNZ , are evaluated by using the defect records of four Web application projects developed by a Turkish software organization. 100%, 70% and 50% of the recorded data is used as input to the maximum likelihood parameter estimation MLPE method and the results of these three cases are investigated and commented separately in the research. The goodness of fit and the predictive validity of the models to the project data are tested by calculating Mean Square Error MSE , Mean Magnitude Relative Error MMRE , Percentage Relative Error Deviation PRED and Average Balanced Predicted Relative Error A.BPRE measures. For each NHPP model 48 separate cases which are combinations of the three defect inflow data cases 100%, 70% and 50% , four projects and four measures, are investigated and ranked. It is shown that the NHPP models can be applied to Web applications and Delayed S-shaped model displays the best results among the alternatives. However, it is understood that the Goel-Okumoto and Yamada models give identical results and that these two models converge to each other with respect to the project defect data that has been used. Combined, these two models obtain the highest ranking scores and it is concluded that these two models perform better than the other models with respect to Web based software.

Neural Networks for Flow Bottom Hole Pressure Prediction

Installation of down-hole gauges in oil wells to determine Flowing Bottom-Hole Pressure (FBHP) is a dominant process especially in wells lifted with electrical submersible pumps. However, intervening a well occasionally is an exhaustive task, associated with production risk, and interruption. The previous empirical correlations and mechanistic models failed to provide a satisfactory and reliable tool for estimating pressure drop in multiphase flowing wells. This paper aims to find the optimum parameters of Feed-Forward Neural Network (FFNN) with back-propagation algorithm to predict the flowing bottom-hole pressure in vertical oil wells. The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and for the sake of qualitative comparison, Radial Basis neural and network and the empirical modes have been developed. The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 12.5% of the total data sets to give 3.4 RMSE and 97% of the test data achieved 90% accuracy.

Neural Networks for Flow Bottom Hole Pressure Prediction

Installation of down-hole gauges in oil wells to determine Flowing Bottom-Hole Pressure (FBHP) is a dominant process especially in wells lifted with electrical submersible pumps. However, intervening a well occasionally is an exhaustive task, associated with production risk, and interruption. The previous empirical correlations and mechanistic models failed to provide a satisfactory and reliable tool for estimating pressure drop in multiphase flowing wells. This paper aims to find the optimum parameters of Feed-Forward Neural Network (FFNN) with back-propagation algorithm to predict the flowing bottom-hole pressure in vertical oil wells. The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and for the sake of qualitative comparison, Radial Basis neural and network and the empirical modes have been developed. The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 12.5% of the total data sets to give 3.4 RMSE and 97% of the test data achieved 90% accuracy.

Experimental and modeling studies of methylene blue adsorption onto particles of peanut shell

ABSTRACTIn this study, adsorption of methylene blue from aqueous solution onto peanut shell has been investigated. Experimental data obtained from dye adsorption have been studied by five two-parameter (Langmuir, Freundlich, Dubinin–Radushkevich, Temkin, and Frumkin) and five three-parameter (Redlich–Peterson, Sips, Toth, Radke–Prausnitz, and Koble–Corrigan) isotherm models. Eight nonlinear error functions (sum squares errors, hybrid fractional error function, Marquardt’s percent standard deviation, average relative error, sum of absolute error, the coefficient of determination, nonlinear chi-square test, and standard deviation of relative errors) were examined for isotherm equations. In order to explain the adsorption kinetics fit of pseudo-first-order, pseudo-second-order, Elovich, and intraparticle kinetic models were used. The best fitting isotherm model was the Radke–Prausnitz and kinetics model was pseudo-second-order. Adsorption experiment showed that the maximum capacity of agricultural waste for methylene blue was 67.57 L · g−1 at 25°C.

Removal of Amoxicillin Antibiotic from Aqueous Solution Using an Anionic Surfactant

A novel separation technique based on an aqueous surfactant extraction to remove organic contaminants from aqueous solutions was investigated. A model was developed regarding the kinetic partitioning of amoxicillin and regarding the mechanism governing the forward transfer of amoxicillin in a reverse micelle system. Results were interpreted in terms of a two-film theory for flat interface. To confirm the relevance of the developed separation technique, it was applied to the elimination of amoxicillin by adsorption on an anionic surfactant, sodium dodecyl sulfate. The effects of various parameters such as contact time, pH, temperature, and initial concentration of sodium dodecyl sulfate were investigated at an agitation speed of 350 rpm. The percentage of maximum adsorption capacity of amoxicillin was found to be 87.7 % for the following optimal conditions: amoxicillin concentration of 4 mg/L, 40 min contact time, pH 4, 50 °C, and 0.01 g/L initial sodium dodecyl sulfate concentration. The results showed that the pseudo-first-order model provides most adequate correlation of experimental data compared to the pseudo-second-order model. Three statistical functions were used to estimate the error deviations between experimental and theoretically predicted kinetic adsorption values, including the average relative error deviation (ARED), the sum of the squares of the errors (SSE), and the standard deviation of residuals (S res). The results showed that, both Freundlich equation and pseudo-first-order equation provide the best fit to experimental data. Adsorption isotherm data appeared to be accurately described by a Freundlich model. The thermodynamic parameters (∆G, ∆H, and ∆S) showed that the process was feasible, spontaneous, and exothermic.

A hybrid wavelet analysis–cloud model data‐extending approach for meteorologic and hydrologic time series

AbstractFor scientific and sustainable management of water resources, hydrologic and meteorologic data series need to be often extended. This paper proposes a hybrid approach, named WA‐CM (wavelet analysis–cloud model), for data series extension. Wavelet analysis has time‐frequency localization features, known as “mathematics microscope,” that can decompose and reconstruct hydrologic and meteorologic series by wavelet transform. The cloud model is a mathematical representation of fuzziness and randomness and has strong robustness for uncertain data. The WA‐CM approach first employs the wavelet transform to decompose the measured nonstationary series and then uses the cloud model to develop an extension model for each decomposition layer series. The final extension is obtained by summing the results of extension of each layer. Two kinds of meteorologic and hydrologic data sets with different characteristics and different influence of human activity from six (three pairs) representative stations are used to illustrate the WA‐CM approach. The approach is also compared with four other methods, which are conventional correlation extension method, Kendall‐Theil robust line method, artificial neural network method (back propagation, multilayer perceptron, and radial basis function), and single cloud model method. To evaluate the model performance completely and thoroughly, five measures are used, which are relative error, mean relative error, standard deviation of relative error, root mean square error, and Thiel inequality coefficient. Results show that the WA‐CM approach is effective, feasible, and accurate and is found to be better than other four methods compared. The theory employed and the approach developed here can be applied to extension of data in other areas as well.

Development of an intelligent model for wax deposition in oil pipeline

Crude oil transport is one important part of the oil industry. Wax deposition is a very complex phenomenon that in recent years is one of the major challenges in oil industry. Wax deposited on the inner surface of crude oil pipelines are capable to reduce or completely stop the oil flow and the oil industry imposing large costs. The main objective of this study was to present a novel approach for predication of wax deposition thickness in single-phase turbulent flow rate. Using experimental data set and Adaptive neural-fuzzy inference system (ANFIS) model was developed. From the results predicted by this model, it can be pointed out that the ANFIS model can be used as powerful tools for prediction of wax deposition thickness in single-phase turbulent flow rate with mean square error, absolute relative deviation error and average absolute deviation error which are 0.00077034, 0.015720 and 0.097961, respectively.

A Novel PSO based Approach with Hybrid of Fuzzy C-Means and Learning Automata in Software Cost Estimation

We used Particle Swarm Optimization (PSO) algorithm hybrid with Fuzzy C-Means (FCM) and Learning Automata (LA) algorithms for Software Cost Estimation (SCE). In this paper we test and evaluate PSO-FCM and PSO-LA hybrid models on NASA dataset software projects. The obtained results showed that in the hybrid models the values of Magnitude of Relative Error (MRE) and Mean Magnitude of Relative Error (MMRE) were reduced compared with COCOMO model and also the accuracy of Percentage of Relative Error Deviation (PRED) was higher in the hybrid models.

A Novel PSO based Approach with Hybrid of Fuzzy C-Means and Learning Automata in Software Cost Estimation

We used Particle Swarm Optimization (PSO) algorithm hybrid with Fuzzy C-Means (FCM) and Learning Automata (LA) algorithms for Software Cost Estimation (SCE). In this paper we test and evaluate PSO-FCM and PSO-LA hybrid models on NASA dataset software projects. The obtained results showed that in the hybrid models the values of Magnitude of Relative Error (MRE) and Mean Magnitude of Relative Error (MMRE) were reduced compared with COCOMO model and also the accuracy of Percentage of Relative Error Deviation (PRED) was higher in the hybrid models.

A kinetic modeling study of phosphate adsorption onto Phoenix dactylifera L. date palm fibers in batch mode

The removal of phosphates from aqueous solutions by adsorption onto date palm fibers (DPF) has been studied in batch mode. The aim of this study was to understand the mechanisms that govern phosphate sorption and find an appropriate model for the kinetics of removal. In order to investigate the mechanism of sorption and potential rate controlling steps, pseudo first-order, pseudo second-order, intra-particle diffusion and the Elovich equations have been used to test experimental data. Kinetic analysis of the four models has been carried out for initial phosphate concentration in the range of 30–110mg/L. The rate constants for the four models have been determined and the correlation coefficients have been calculated in order to assess which model provides the best fit predicted data with experimental results. Seven statistical functions were used to estimate the error deviations between experimental and theoretically predicted kinetic adsorption values, including the average relative error deviation (ARED), Marquardt’s percent standard error deviation (MPSED), the hybrid fractional error function (HYBRID), the sum of the squares of the errors (SSE) and three alternative statistical functions, including the Chi-square test, the F-test and Student’s T-test. The results showed that, both Elovich equation and pseudo second-order equation provide the best fit to experimental data for different initial phosphate concentrations.

Error Anlaysis Studies of Dye Adsorption onto Activated Carbon from Aqueous Solutions

Adsorption of tartrazine and methylene blue from aqueous solution onto activated carbon has been investigated. Experimental datas obtained from dye adsorption have been studied by five two-parameter (Langmuir, Freundlich, Dubinin-Radushkevich, Temkin, and Frumkin), five three-parameter (Redlich-Peterson, Sips, Toth, Radke-Prausnitz, Koble-Corrigan) isotherm models. In this study, eight nonlinear error functions (sum squares errors, hybrid fractional error function, Marquardt's percent standard deviation, average relative error, sum of absolute error, the coefficient of determination, nonlinear chi-square test, and standard deviation of relative errors) were examined for isotherm equations. In order to clarify the adsorption kinetics fit of pseudo-first order and pseudo-second order kinetic models were used. The best fitting isotherm model was the Radke-Prausnitz and kinetics model was pseudo-second order for two dyes. Adsorption experiment showed that the maximum capacity of activated carbon's for methylene blue and tartrazine were 28.571 and 18.182 mg.g−1, respectively.

PEMBUATAN ALAT UKUR KECEPATAN RESPON MANUSIA BERBASIS MIKROKONTROLLER AT 89S8252

A human movement as a response of something is always different for each individual. However, the response is often difficult to measure because it is very flexible and sometimes are very subjective. Research was conducted with the goal to design and create tools measure the response speed of human-based mikrokontroller AT89S8252 and determine the level of accuracy tool. Measu rement tool consists of 4 switch, ie, 2, and for tester 2 for participants who tested, 2 indicator LED, buzzer, 4 buttons (menu, enter, select and cancel), speaker, and MK AT89S8252. LCD as a display. For the data on the motor response and eye response, motor ears done 9 times as much for each individual. Before the test on the motor response, eye and ear motor is done, first time on the tool dikalibrasikan using the time on the stopwatch 1 / 1000. After that the process of testing carried out by determining the response to be conducted, the examiner pressing switch S1 or S2 switch, then LED1 or LED2 akan buzzer or light simultaneously. Participants must be tested first with a quick switch press switch S3 or S4 to stop the buzzer and the process of calculation. Step response of the data on the eyes and ears are the same. In the process of calibration equipment with time using the stopwatch 1 / 1000 data obtained 9. The relative error deviation of 2.5% was obtained, the accuracy produced by means of measuring the speed of human response, 97.5%. Boundary condition is the human response that is the limit of 3 is very good (one limit), normal / good (limit two), poor (limit three). In the testing of motor response in humans that use LED indicator, the data better than a motor ear response data, is due to the rapid Rambat different. LED light will reach the human eye without the lapse of time, but the sound or the sound will reach the human ear after the time interval. <br /><br />Kata Kunci:Kecepatan, Gerak, Respon, Mikrokontroller<br /><br />

Applicability of some statistical tools to predict optimum adsorption isotherm after linear and non-linear regression analysis

In any single component isotherm study, determining the best-fitting model is a key analysis to mathematically describe the involved sorption system and, therefore, to explore the related theoretical assumptions. Hence, several error calculation functions have been widely used to estimate the error deviations between experimental and theoretically predicted equilibrium adsorption values ( Q e , exp vs . Q e , theo as X- and Y-axis, respectively), including the average relative error deviation, the Marquardt's percent standard error deviation, the hybrid fractional error function, the sum of the squares of the errors, the correlation coefficient and the residuals. In this study, five other statistical functions are analysed to investigate their applicability as suitable tools to evaluate isotherm model fitness, namely the Pearson correlation coefficient, the coefficient of determination, the Chi-square test, the F-test and the Student's T-test, using the commonly-used functions as references. The adsorption of textile dye onto Posidonia oceanica seagrass fibres was carried out, as study case, in batch mode at 20 °C. Besides, and in order to get an overall approach of the possible utilization of these statistical functions within the studied item, the examination was realized for both linear and non-linear regression analysis. The related results showed that, among the five studied statistical tools, the χ 2 and Student's T-tests were suitable to determine the best-fitting isotherm model for the case of linear modelling approach. On the other hand, dealing with the non-linear analysis, despite the Student's T-test, all the other functions gave satisfactorily results, by agreeing the commonly-used error functions calculation.

Evaluation of Thermal Properties of Food Materials at High Pressures Using a Dual‐Needle Line‐Heat‐Source Method

Thermal properties of food systems at high pressure (HP) are important in the design and operation of HP processing equipment. Available techniques for thermal property evaluation under HP conditions are still very limited. In this study, a dual-needle line-heat-source (DNL) device was installed in an HP vessel to evaluate thermal conductivity (k), diffusivity (alpha), and volumetric heat capacity (C(pV)) of foods at high pressure. The DNL probe was calibrated using glycerin (0.1 MPa) and 2% (w/w) agar gel (0.1 to 350 MPa) at 5 and 25 degrees C. Calibration results showed a good correlation with the reference data of pure water: R(2)= 0.966 for thermal conductivity and R(2)= 0.837 for diffusivity, and a small standard deviation of relative error (3.18%) for the volumetric heat capacity. Fresh potato and cheddar cheese were used as test samples at 5 degrees C at selected pressure levels (0.1 to 350 MPa). The potato samples gave thermal properties very close to those of pure water, but much higher than those of the cheese. The k and alpha values of both potato and cheese increased with pressure and a 2nd-order polynomial well fitted their pressure dependency. The volumetric heat capacity data did not show a clear pressure-dependency trend. The experimental system worked well for the evaluation of thermal properties at pressures up to 350 MPa.