Modeling the Drying Kinetics of Green Bell Pepper in a Heat Pump Assisted Fluidized Bed Dryer

In this research, green bell pepper was dried in a pilot plant fluidized bed dryer equipped with a heat pump humidifier using three temperatures of 40, 50 and 60C and two airflow velocities of 2 and 3m/s in constant air moisture. Three modeling methods including nonlinear regression technique, Fuzzy Logic and Artificial Neural Networks were applied to investigate drying kinetics for the sample. Among the mathematical models, Midilli model with R=0.9998 and root mean square error (RMSE)=0.00451 showed the best fit with experimental data. Feed-Forward-Back-Propagation network with Levenberg-Marquardt training algorithm, hyperbolic tangent sigmoid transfer function, training cycle of 1,000 epoch and 2-5-1 topology, deserving R=0.99828 and mean square error (MSE)=5.5E-05, was determined as the best neural model. Overall, Neural Networks method was much more precise than two other methods in prediction of drying kinetics and control of drying parameters for green bell pepper. Practical Applications: This article deals with different modeling approaches and their effectiveness and accuracy for predicting changes in the moisture ratio of green bell pepper enduring fluidized bed drying, which is one of the most concerning issues in food factories involved in drying fruits and vegetables. This research indicates that although efficiency of mathematical modeling, Fuzzy Logic controls and Artificial Neural Networks (ANNs) were all acceptable, the modern prediction methods of Fuzzy Logic and especially ANNs were more productive and precise. Besides, this report compares our findings with previous ones carried out with the view of predicting moisture quotients of other food crops during miscellaneous drying procedures. © 2016 Wiley Periodicals, Inc

Modeling some drying characteristics of sour cherry (Prunus cerasus L.) under infrared radiation using mathematical models and artificial neural networks

The effect of air temperature, air velocity and infrared (IR) radiation on the drying kinetics of sour cherry was investigated using a laboratory infrared dryer.  Experiments were conducted at air temperatures of 35, 50 and 65°C, air velocities of 0.5, 1.1 and 1.7 m/s and IR radiations of 500, 1,000 and 1,500 W.  Five empirical drying models for describing time dependence of the moisture ratio change were fitted to experimental data.  Artificial neural network (ANN) method was used to predict the effective moisture diffusivity and specific energy consumption of the samples.  Among the applied models, Midilli et al. model was the best to predict the thin layer drying behavior of sour cherry.  Effective moisture diffusivity of sour cherry varied between 1.17×10-10 and 8.13×10-10 m2/s.  Activation energy of sour cherry was in the range of 30.31–     41.68 kJ/mol.  Specific energy consumption was in the range of 56.12–891.16 MJ/kg.  After well training of the ANN models, it proved that the ANN model was relatively better than the empirical models.  The best neural network feed and cascade forward back-propagation topologies for the prediction of effective moisture diffusivity and energy consumption were the 3-2-3-1 and 3-3-3-1 structures with the training algorithm of trainlm and threshold functions of tansig, tansig-logsig-tansig, respectively.  The best R2 value for predication of moisture diffusivity and energy consumption were 0.9944 and 0.9905, respectively.    Keywords: sour cherry, drying, effective moisture diffusivity, activation energy, artificial neural networ

Modelling the convective drying process of pumpkin (Cucurbita moschata) using an artificial neural network

This study investigated the drying kinetic of pumpkin under different drying temperatures (50, 60, 70 and 80°C), samples thickness (3, 4, 5 and 7mm), air velocity (1.2m/s) and relative humidity (40 - 50%). Kinetic models were developed using semi-theoretical thin layer models and multi-layer feed-forward artificial neural network (ANN) method. The Hii et al. (2009) semi-theoretical model was found to be the most suitable thin layer model while two hidden layers with 20 neurons was the best for the ANN method. The selections were based on the statistical indicators of coefficient of determination (R2), root mean square error (RMSE) and sum of squares error (SSE). Results indicated that the ANN demonstrated better prediction than those of the theoretical models with R2, RMSE and SSE values of 0.992, 0.036 and 0.207 as compared to the Hii et al. (2009) model values of 0.902, 0.088 and 1.734 respectively. The validation result also showed good agreement between the predicted values obtained from the ANN model and the experimental moisture ratio data. This indicates that an ANN can effectively describe the drying process of pumpkin

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Novi pristup u predviđanju izravnog i otvorenog solarnog sušenja pomoću umjetne neuronske mreže za ljekovito bilje

In this study, an artificial neural network (ANN) was developed to obtain a generalized model for predicting the direct and open sun drying process for some medicinal plants. Since the quality of the experimental dataset can lead to a very performant model, in this study the dataset was collected from previously published papers and divided randomly into three subsets, namely 70 %, 15 %, and 15 % for training, testing, and validation. Based on the complex solar drying behaviour, ten parameters were considered as inputs: time, global solar radiation (GSR), outside temperature, inclination, emissivity, altitude, longitude, latitude, inside temperature, and nutritional value, to predict moisture content (MC), and drying rate (DR). Based on a trial and error method, the best ANN model was found with a topology of 10-28-14-2, with regression coefficient and root mean square error of (R = 97.044 %. RMSE = 4.589 %) and (R = 99.968 %, RMSE = 1.185 %) for MC and DR, respectively. It can be concluded that the obtained ANN model provides the best method for solar dryer modelling which can be generalized for any location in the world. This work is licensed under a Creative Commons Attribution 4.0 International License.U ovom istraživanju razvijena je umjetna neuronska mreža (ANN) za dobivanje uopćenog modela za predviđanje izravnog i otvorenog postupka solarnog sušenja za određene ljekovite biljke. Budući da kvaliteta eksperimentalnog skupa podataka može dovesti do modela visoke izvedbe, u ovoj je studiji skup podataka prikupljen iz prethodno objavljenih radova i nasumce podijeljen u tri podskupine – 70 % za trening, 15 % za testiranje i 15 % za validaciju. Na temelju složenog postupka solarnog sušenja, za predviđanje sadržaja vlage (SV) i brzine sušenja (BS) uzima se deset ulaznih parametara: vrijeme, globalno sunčevo zračenje (GSZ), vanjska temperatura, nagib, emisivnost, nadmorska visina, zemljopisna dužina, zemljopisna širina, unutarnja temperatura i hranjiva vrijednost. Na temelju metode pokušaja i pogreške, pronađen je najbolji model ANN s topologijom 10-28-14-2 te koeficijentom regresije i srednjom kvadratnom pogreškom od (R = 97,044 %, RMSE = 4,589 %) za SV i (R = 99,968 %, RMSE = 1,185 %) za BS. Može se zaključiti da je dobiveni ANN najbolji model za modeliranje solarnih sušilica koji se može generalizirati za bilo koje mjesto na svijetu. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Prediction of Physical Parameters of Pumpkin Seeds Using Neural Network

The design of the machines and equipment used in harvest and post-harvest processing should be compatible with the physical, mechanical and rheological characteristics of the fruits and vegetables. In machine design for agricultural products, several characteristics of relevant products and seeds should be known ahead. Designers can either measure all these design parameters one by one, or they may use intelligent systems to estimate such parameters. Neural networks (NNs) are new computational tools that provide a quick and accurate means of physical properties prediction of agricultural materials, and have been shown to perform well in comparison with traditional methods. In this research, some physical properties of pumpkin (Cucurbita pepo L.) seeds, including linear dimensions, volume, surface and projected area, geometric mean diameter and sphericity were calculated tridimensional in lab conditions. Then, prediction of these parameters was carried out using NNs. The research was divided into two parts; experimental investigation and simulation analysis with NNs. Back Propagation Neural Network (BPNN) and Radial Basis Neural Network (RBNN) structures were employed to estimate physical parameters of the pumpkin seeds. The Root Mean Squared Error (RMSE) was 0.6875 for BPNN and 0.0025 for RBNN structures. The RBNN structure was superior in prediction and could be used as an alternative approach to conventional methods

The use of artificial neural networks (ANN) in food process engineering

Artificial neural networks (ANN) aim to solve problems of artificial intelligence, by building a system with links that simulate the human brain. This approach includes the learning process by trial and error. The ANN is a system of neurons connected by synaptic connections and divided into incoming neurons, which receive stimulus from the external environment, internal or hidden neurons and output neurons, that communicate with the outside of the system. The ANNs present many advantages, such as good adaptability characteristics, possibility of generalization and high noise tolerance, among others. Neural networks have been successfully used in various areas, for example, business, finance, medicine, and industry, mainly in problems of classification, prediction, pattern recognition and control. In the food industry, food processing, food engineering, food properties or quality control, statistical tools are frequently present, and ANNs can process more efficiently data comprising multiple input and output variables. The objective of this review was to highlight the application of ANN to food processing, and evaluate its range of use and adaptability to different food systems. For that a systematic review was undertaken from the scientific literature and the selection of the information was based on inclusion criteria defined. The results indicated that ANN is widely used for modelling and prediction in food systems, showing good accuracy and applicability to a wide range of situations and processes in food engineering.info:eu-repo/semantics/publishedVersio

Screening of the aerodynamic and biophysical properties of barley malt

An understanding of the aerodynamic and biophysical properties of barley malt is necessary for the appropriate design of equipment for the handling, shipping, dehydration, grading, sorting and warehousing of this strategic crop. Malting is a complex biotechnological process that includes steeping; ger-mination and finally, the dehydration of cereal grains under controlled temperature and humidity conditions. In this investigation, the biophysical properties of barley malt were predicted using two models of artificial neural networks as well as response surface methodology. Stepping time and germination time were selected as the independent variables and 1 000 kernel weight, kernel density and terminal velocity were selected as the depen-dent variables (responses). The obtained outcomes showed that the artificial neural network model, with a logarithmic sigmoid activation function, presents more precise results than the response surface model in the prediction of the aerodynamic and biophysical properties of produced barley malt. This model presented the best result with 8 nodes in the hidden layer and significant correlation coefficient values of 0.783, 0.767 and 0.991 were obtained for responses one thousand kernel weight, kernel density, and terminal velocity, respectively. The outcomes indicated that this novel technique could be successfully applied in quantitative and qualitative monitoring within the malting process

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain