Effect of conical air distributors on drying of peppercorns in a fluidized bed dryer: Prediction using an artificial neural network

Chuwattanakul, V.; Wongcharee, K.; Pimsarn, Monsak; Chokphoemphun, Suriya; Chamoli, Sunil; Eiamsa-ard, Smith

doi:10.1016/j.csite.2022.102188

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…Previous works only presented the influence of different drying tray layers on the behavior and/or characteristics of the drying material. [38] Previous studies demonstrated that high turbulence/swirl of hot air injected into fluidized bed dryers resulted in enhanced drying rates. In the present work, a fluidized bed dryer with conical air distributors consisting of a solid bottom cone and a perforated cone on top of a perforated sheet was applied for drying enhancement.…”

Section: Referencementioning

confidence: 99%

Review of Energy-Related Machine Learning Applications in Drying Processes

Đaković,

Kljajić,

Milivojević

et al. 2023

Energies

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Drying processes are among the most energy-intensive industrial processes. There is a need for development of the efficient methods needed for estimating, measuring, and reducing energy use. Different machine learning algorithms might provide some of the answers to these issues in a faster and less costly way, without the need for time-consuming and expensive experiments done at different scales of the dryers. The aim of this paper was to provide a comprehensive overview of machine learning applications for addressing energy-related challenges by exploration of different energy types and energy reduction opportunities. Also, the analysis of the applied algorithms, their specific applications and a critical evaluation of the obtained results are provided. The paper is based on the necessity of the improvements in energy use needed for drying related on the existing data. The overview of the ways for such achievements, and a general classification of machine learning algorithm are the background of the paper. The methods used are the machine learning techniques employed in different energy-related issues for drying processes. The paper focuses on the applications of artificial neural networks and other machine learning algorithms and models for different energy-related issues, including different energy types applications, challenges associated with energy consumption, and opportunities for energy reduction. Not only the applied algorithms, but also their specific applications, and the statistical analysis of the obtained results are also overviewed. Finally, a critical evaluation of the findings highlighting the potentials of machine learning algorithms in addressing energy-related challenges (such as estimation of energy consumption, opportunities for energy reduction, and use of different energy sources) is provided. The presented analysis underscored the effectiveness of machine learning applications for these purposes.

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Section: Referencementioning

confidence: 99%

Review of Energy-Related Machine Learning Applications in Drying Processes

Đaković,

Kljajić,

Milivojević

et al. 2023

Energies

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“…The two-stage evaporator heat pump dryer demonstrates a 35% increase in heat recovery compared to the single-stage evaporator heat pump dryer [11]. Utilizing a neural network, it is determined that the conical air distributor achieves uniform air distribution and results in a faster drying rate compared to a flat plate distributor during the initial 60 minutes of drying [12]. The combined air-to-air heat exchanger and R134a heat pump system outperforms the air-to-air heat exchanger alone [13].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Tray, Bed and Integrated Drying Chamber in Closed Loop Heat Pump Dryer for Bermuda Grass

M¹,

Kumaresan

Sivagaminathan³

et al. 2023

E3S Web Conf.

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Drying plays a crucial role in various industries such as food production, agriculture, Siddha, Ayurveda, and medical fields. To achieve controlled drying conditions, a heat pump dryer is considered an effective method, allowing for precise control of parameters like temperature, humidity, and air velocity. In this study, a heat pump dryer was designed and constructed to investigate the drying characteristics of Bermuda grass (Cynodon dactylon) at different velocities (1.5 m/s, 2.0 m/s, and 2.5 m/s) using three types of drying chambers: fluidized bed dryer, tray dryer, and combined dryer (a combination of bed and tray). The heat pump system utilized R134a as the refrigerant. The performance of the heat pump dryer in the three drying chambers was analyzed using Bermuda grass as the drying product. The Moisture Removal Rate (MRR) was calculated for various combinations of velocity and drying chamber, and it was observed that the combined dryer achieved a higher MRR at all three velocities compared to the tray and fluidized bed dryers.

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“…Pembuatan Smart Farming bertujuan untuk mengurangi ketergantungan campur tanggan manusia dalam proses budidaya jamur, serta membuat proses perawatan jamur lebih optimal karena suhu udara didalam kumbung jamur tetap terjaga pada suhu yang sesuai [13].…”

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Smart Farming Untuk Pengaturan Suhu Ruangan Pada Budidaya Jamur Tiram Berbasis Backpropagation

Sugiartawan

Desnanjaya

2022

Indonesian J. Electron. Instrum. Syst.

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The problem with mushroom cultivation is the difficulty of regulating the room temperature of mushrooms, especially oyster mushrooms. The optimal production of oyster mushrooms is at temperatures between 25 C - 27 C. To regulate or manipulate humidity and room temperature to water the kumbung or mushroom room. The watering process is carried out several times to stabilize the room temperature during the day.To overcome the watering that is done manually, Automatic Temperature Control and Monitoring of Oyster Mushrooms Based on GSM Sim800l Arduino Uno is made. This tool uses a DHT11 sensor, relay, 16x2 LCD, GSM Sim 800L, and Stepdown. The test was carried out in a mushroom kumbung measuring 10.7m long, 5.9m wide, and 3.5m high. Watering time is done by observing the data at room temperature. The data is then studied using a backpropagation. This method aims to identify the pattern of watering time so that the optimal watering time is produced. The test results show that the tool can monitor the temperature and humidity of the kumbung mushroom with the following values: temperature 27°C - 33°C and humidity 70% - 90%. The introduction of mushroom watering patterns with BPNN showed an error rate of 40%.

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Effect of conical air distributors on drying of peppercorns in a fluidized bed dryer: Prediction using an artificial neural network

Cited by 6 publications

References 16 publications

Review of Energy-Related Machine Learning Applications in Drying Processes

Review of Energy-Related Machine Learning Applications in Drying Processes

Performance Comparison of Tray, Bed and Integrated Drying Chamber in Closed Loop Heat Pump Dryer for Bermuda Grass

Smart Farming Untuk Pengaturan Suhu Ruangan Pada Budidaya Jamur Tiram Berbasis Backpropagation

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