Neuro-fuzzy modeling of a conveyor-belt grain dryer

The grain drying process is one of the most critical post-harvest operations in modern agricultural production. Development of a reliable control strategy for this process plays an important role in improving the overall efficiency and productivity of the drying process. In control system design, the first problem to be addressed is the availability of a relatively simple and accurate model of the process to be controlled. However, the majority of the models developed for the grain drying process and the numerical methods required to solve them are characterized by their highly complex nature, and thus they are not suitable to be utilized in control system design. This paper presents an application of a neuro-fuzzy system, in particular the adaptive neuro-fuzzy inference system (ANFIS), to develop a data-driven model for a conveyor-belt grain dryer. This model can be easily used in control system design to develop a reliable control strategy for the drying process. By conducting a real-time experiment to dry paddy grains, a set of input-output data were collected from a laboratory-scale conveyor-belt grain dryer. These data were then presented to the ANFIS network in order to learn the nonlinear functional relationship between the input and output data by this network. Based on utilizing a clustering method to identify the structure of the ANFIS network, the resulting ANFIS model has shown a remarkable modeling performance to represent the drying process. In addition, the modeling result achieved by this ANFIS model was compared with those of an autoregressive with exogenous input (ARX) model and an artificial neural network (ANN) model, and the results clearly showed the superiority of the ANFIS model

Автоматизация процесса сушки зерна в шахтных зерносушилках на основе теории нечетких множеств

Представлено обоснование системы управления процессом сушки зерна в шахтных зерносушилках на основании нечетких множеств.Представлено обґрунтування системи управління процесом сушіння зерна в шахтних зерносушарках на основі нечітких множин.Substantiation the process control system of drying grain in the silo dryers based on fuzzy set is represented

Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems

The grain drying process is characterized by its complex and non-linear nature. As a result, conventional control system design cannot handle this process appropriately. This work presents an intelligent control system for the grain drying process, utilizing the capabilities of the adaptive neuro-fuzzy inference system (ANFIS) to model and control this process. In this context, a laboratory-scale conveyor-belt grain dryer was specifically designed and constructed for this study. Utilizing this dryer, a real-time experiment was conducted to dry paddy (rough rice) grains. Then, the input–output data collected from this experiment were presented to an ANFIS network to develop a control-oriented dryer model. As the main controller, a simplified proportional–integral–derivative (PID)-like ANFIS controller is utilized to control the drying process. A real-coded genetic algorithm (GA) is used to train this controller and to find its scaling factors. From the robustness tests and a comparative study with a genetically tuned conventional PID controller, the simplified ANFIS controller has proved its remarkable ability in controlling the grain drying process represented by the developed ANFIS model

A GODFIP Control Algorithm for an IRC Grain Dryer

Drying is an energy intensive and complex nonlinear process and it is difficult to control and make the traditional control meet the challenges. In order to effectively control the output grain moisture content of a combined infrared radiation and convection (IRC) grain dryer, taking into account the superiority of the fuzzy control method in dealing with complex systems, in this article, a genetic optimization dual fuzzy immune PID (Proportional-Integral-Derivative) (GODFIP) controller was proposed from the aspects of energy savings, stability, accuracy, and rapidity. The structure of the GODFIP controller consists of two fuzzy controllers, a PID controller, an immune algorithm, and a genetic optimization algorithm. In addition, a NARX model which can give relatively good predictive output information of the IRC dryer was established and used to represent the actual drying process to verify the control performance in the control simulation and anti-interference tests. The effectiveness of this controller was demonstrated by computer simulations, and the anti-interference performance comparative study with the other controllers further confirmed the superiority of the proposed grain drying controller which has the least value of performance objective function, the shortest rising time, and the best anti-interference ability compared to the other three compared controllers

Control of a solar dryer through using a fuzzy logic and low-cost model-based sensor

Solar drying is one of the important processes used for extending the shelf life of agricultural products. Regarding consumer requirements, solar drying should be more suitable in terms of curtailing total drying time and preserving product quality. Therefore, the objective of this study was to develop a fuzzy logic-based control system, which performs a ?human-operator-like? control approach through using the previously developed low-cost model-based sensors. Fuzzy logic toolbox of MatLab and Borland C++ Builder tool were utilized to develop a required control system. An experimental solar dryer, constructed by CONA SOLAR (Austria) was used during the development of the control system. Sensirion sensors were used to characterize the drying air at different positions in the dryer, and also the smart sensor SMART-1 was applied to be able to include the rate of wood water extraction into the control system (the difference of absolute humidity of the air between the outlet and the inlet of solar dryer is considered by SMART-1 to be the extracted water). A comprehensive test over a 3 week period for different fuzzy control models has been performed, and data, obtained from these experiments, were analyzed. Findings from this study would suggest that the developed fuzzy logic-based control system is able to tackle difficulties, related to the control of solar dryer process

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

Perancangan Sistem Kendali Suhu Pada Mesin Pengering Hybrid Menggunakan Metode Fuzzy Logic

Food security is condition related to food supply sufficient in quantity and quality, safety, diversity, nutritious content, equitability and affordability. Post harvest handling of paddy is a very strategic effort in order to support the increase of rice production and food security. Drying is an activity on post harvest that aimed to reduce water content. Various type of paddy\u27s drying machine has been constructed to enhance drying process. However, most drying machine constructed in large dimension and operated using fossil fuel. To overcome these problems, hybrid technology is proposed, i.e. grain-drying machine using combination of solar and biomass energy. This machine is equipped with fuzzy logic control system using microcontroller Arduino Mega 2560 R3 as fan velocity control center based on reading of sensor HT11 that able to detect temperature and humidity in drying room also sensor K Type thermocouple Max6675 that detect temperature in combustion chamber and heat exchanger. This research aimed to support continuity of drying process in order to determine each sensor\u27s period to achieve their setting point. Based on 90 minutes trial period, the result show maximum temperature reduction 2,17% (wet basis), maximum temperature 50ºC, setting point for temperature (45ºC) achieved in 60 minutes, minimum humidity 18%, and setting point for humidity achieved in 30 minutes

Development and evaluation of an adaptive neuro fuzzy interface models to predict performance of a solar dryer

 This research is carried out to predict energy efficiency of a solar dryer by adaptive neuro-fuzzy inference system (ANFIS) model. In this model, temperatures in the collector inlet, collector outlet and in the dry chamber exit and also absorbed heat energy by collector and necessary energy for evaporation of product moisture were considered as an ANFIS network inputs. To investigate the capability of ANFIS models in prediction of dryer efficiency, empirical model and regression analysis were used and their results were compared by ANFIS models. To evaluate an accuracy ANFIS models, statistical parameters such as mean absolute error, mean squared error, sum squared error, correlation coefficient (R) and probability (P) were calculated. Results indicated that coefficient of determination for ANFIS model was higher than empirical model and regression analysis whereas amounts of SSE and MSE were lower. From the results of this research, it is concluded that ANFIS model represent energy efficiency better than empirical model and regression analysis. Finally, it can be stated that the ANFIS model could be efficient in to determining the energy efficiency in a forced-convection solar dryer