Genetically Optimized PID Controller for a Novel Corn Dryer

Abhigna, Raptadu; Sharma, Akshat; Kavya, Kudumuu; Sharma, Pankaj; Salkuti, Surender Reddy; Kumar, Manish; Naidu, Rani Chinnappa; Sreekeessoon, Bhamini

doi:10.1007/978-981-19-6525-8_41

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Relying solely on output feedback control for a system with pure lag characteristics leads to a significant overshoot, longer adjustment time, and poor control performance [ 45 ]. These findings align with previous studies that concluded that output feedback PID controllers [ 46 , 47 ] are not suitable for deep-bed flow-layer drying systems.…”

Section: Discussionsupporting

confidence: 92%

Deep-Learning-Based Model Predictive Control of an Industrial-Scale Multistate Counter-Flow Paddy Drying Process

Zhang,

Fang,

et al. 2023

Foods

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In practical industrial-scale paddy drying production, manual empirical operation is still widely used for process control. This often leads to poor uniformity in the moisture content distribution of discharged grains, affecting product quality. Model Predictive Control (MPC) is considered the most effective control method for paddy drying, but its implementation in industrial-scale drying is hindered by its high computational cost. This study aims to address this challenge by proposing a deep-learning-based model predictive control (DL-MPC) strategy for paddy drying. By establishing a mapping relation between the inlet and outlet paddy moisture content and paddy flow velocity, a DL-MPC strategy suitable for multistage counter-flow paddy drying systems is proposed. DL-MPC systems are developed using long short-term memory (LSTM) neural networks and trained using datasets from single-drying-stage and multistage drying systems. Simulation and analysis are conducted, followed by verification experiments on a 5HNH-15 multistage counter-flow paddy dryer. The results show that the DL-MPC system significantly improves computational speed while achieving satisfactory control performance. The predicted paddy flow velocity exhibits a smooth variation and matches field data obtained from multiple transition points, confirming the effectiveness of the designed DL-MPC system. The mean absolute error between the predicted and actual paddy moisture content under the DL-MPC system is 0.190% d.b., further supporting the effectiveness of the control system.

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Section: Discussionsupporting

confidence: 92%

Deep-Learning-Based Model Predictive Control of an Industrial-Scale Multistate Counter-Flow Paddy Drying Process

Zhang,

Fang,

et al. 2023

Foods

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Design and Testing of Holagen Incandescent Lamp-Based Corn Drying Cabinet to Improve Efficiency and Cleanliness of the Processing Process

Purnomo,

Fzahruddin

2024

IJM

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Indonesia is a tropical country with entrepreneurs engaged in maize cultivation, most of whom live in rural areas. The corn processing process is heavily reliant on sunshine, however owing to the unpredictable environment and sunlight, which is less effective and hygienic in carrying out the corn drying process, the operation is hampered. As a result, a drying cabinet is created that uses heat from electricity and is specifically intended to accelerate the drying process. This corn drying rack employs incandescent lamps, including bulbs and holagen lamps. In addition, the number of drying racks is identified in order to assess the drying cabinet's capacity for work. After testing the tool with various blower speeds (15,000, 10,000, and 5,000 RPM), the electrical power utilized is 120 watts, which includes 50 watts of bulb/halogen lamp power, 40 watts of heated glass, and 30 watts of blower power. So it is known that holagen incandescent lamps are more successful at distributing work evenly on the shelf within 30 minutes, which can save drying time by 20% to 30%.

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Perancangan dan Pengujian Cabinet Pengering Jagung Berbasis Lampu Pijar Holagen untuk Meningkatkan Efisiensi dan Kebersihan Proses Pengolahan

Purnomo,

Fzahruddin

2024

Innovative

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Indonesia merupakan salah satu negara beriklim tropis dimana terdapat pengusaha yang bergerak dibidang pertanian jagung banyak ditemui di pedesaan. Dalam proses pengolahan jagung sangat bergantung pada sinar matahari namun dikarenakan iklim yang tidak menentu ditambah sinar matahari yang kurang efisien dan higienis dalam melakukan proses pengeringan jaung menyebabkan terhambatnya proses pengolahan jagung. Untuk itu dilakukannya perancangan cabinet pengering yang memanfaatkan panas dari listrik yang didesain khusus untuk mempercepat proses pengeringan. Rak pengering jagung ini memanfaatkan lampu pijar baik bohlam maupun lampu holagen. Selain itu jumlah rak pengering juga di identifikasi sehingga mengetahui kemampuan daya tampung cabinet pengering dalam melakukan kerja. Setelah dilakukannya pengujian alat dengan variasi kecepatan blower sebesar yaitu 15.000, 10.000, dan 5.000 Rpm sedangkan daya listrik yang digunakan adalah 120 watt yaitu 50 watt daya lampu bohlam/halogen, 40 watt heater glass dan 30 watt daya blower. Maka diketahui lampu pijar holagen lebih efektif dalam melakukan kerja yang merata diseluruh bagian rak dalam waktu 30 menit yang dapat mempengaruhi laju pengeringan sebesar 20% hingga 30%.

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Genetically Optimized PID Controller for a Novel Corn Dryer

Cited by 3 publications

References 18 publications

Deep-Learning-Based Model Predictive Control of an Industrial-Scale Multistate Counter-Flow Paddy Drying Process

Deep-Learning-Based Model Predictive Control of an Industrial-Scale Multistate Counter-Flow Paddy Drying Process

Design and Testing of Holagen Incandescent Lamp-Based Corn Drying Cabinet to Improve Efficiency and Cleanliness of the Processing Process

Perancangan dan Pengujian Cabinet Pengering Jagung Berbasis Lampu Pijar Holagen untuk Meningkatkan Efisiensi dan Kebersihan Proses Pengolahan

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