Nutrient Solution Acidity Control System on NFT-Based Hydroponic Plants Using Multiple Linear Regression Method

Helmy, Helmy; Janah, Dian Ayu Mifftahul; Nursyahid, Arif; Mara, Muhlasah Novitasari; Setyawan, Thomas Agung; Nugroho, Ari Sriyanto

doi:10.1109/icitacee50144.2020.9239134

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…Multiple linear regression methods are also commonly used to analyze problems where one variable affects multiple variables at the same time [33]. For different problems, a variety of specific analysis methods have been derived, such as: the entry method, deletion method, forward method, backward method, stepwise regression analysis method, etc.…”

Section: Multiple Regression Analysis Modelsmentioning

confidence: 99%

The comprehensive application of the lawn ecology of the football field in enhancing the intention of sports consumption

2023

3C TIC

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With the continuous progress and development of society and the outstanding achievements of football in the field of competitive sports in recent years, the enthusiasm of citizens to participate in sports and fitness has become higher and higher. The course turf ecology is a multi-layered organic whole composed of many natural and social factors, which is an important symbol of football competitiveness. It can be predicted that the relationship between sports and the environment and the impact of environmental changes on consumer intentions are global and long-term. Therefore, according to the annual change status and formation factors of ecological benefits such as carbon sequestration and oxygen release, transpiration water release, and heat absorption in the turf of a football field in a city, the time series stability test method and the multiple regression analysis method are used to discuss the comprehensive application of the lawn ecology of the football field in enhancing the intention of sports consumption. The experimental results verify that the ecological benefits of lawns have a significant positive effect on sports consumption intentions, which can not only optimize the unreasonable sports consumption structure, improve the level of sports consumption, and increase the proportion of consumption amount of more than 5,000 yuan from 5.06% to 23.34%, but also make the leading sports consumption motivation from a more symbolic display of personality, status symbols, and social entertainment to a sportsmanlike physique, entertainment, spiritual enjoyment, and positive guidance of sports consumption motivation.

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Section: Multiple Regression Analysis Modelsmentioning

confidence: 99%

The comprehensive application of the lawn ecology of the football field in enhancing the intention of sports consumption

2023

3C TIC

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“…[12][13][14][15] While no chemical information except the continuous measured pH is needed for PID, a loss of information needs to be accepted since insights into the chemical system cannot be implemented into future pH adjustments, as opposed to model based strategies. More recent pH adjustment approaches are based on MPC arrays, e.g., Altinten et al describe a generalized predictive control for continuous flow pH adjustment 16 , Helmy et al relied on multi-linear regression 17 and Alkamil et al used a fuzzy artificial neural network (ANN) outperforming a PID-control. 18 Others have also expanded on using ML based MPC strategies for this same purpose.…”

Section: Introductionmentioning

confidence: 99%

Automated pH Adjustment Driven by Robotic Workflows and Active Machine Learning

Pomberger

Jose

Walz

et al. 2022

Preprint

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Buffer solutions have tremendous importance in biological systems and in formulated products. Whilst the pH response upon acid/base addition to a mixture containing a single buffer can be described by the Henderson-Hasselbalch equation, modelling the pH response for multi-buffered poly-protic systems after acid/base addition, a common task in all chemical laboratories and many industrial plants, is a challenge. Combining predictive modelling and experimental pH adjustment, we present an active machine learning (ML)-driven closed-loop optimization strategy for automating small scale batch pH adjustment relevant for complex samples (e.g., formulated products in the chemical industry). Several ML models were compared on a generated dataset of binary-buffered poly-protic systems and it was found that Gaussian processes (GP) served as the best performing models. Moreover, the implementation of transfer learning into the optimization protocol proved to be a successful strategy in making the process even more efficient. Finally, practical usability of the developed algorithm was demonstrated experimentally with a liquid handling robot where the pH of different buffered systems was adjusted, offering a versatile and efficient strategy for a pH adjustment processes.

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“…Research [24] designed a nutrient control system on NFTbased hydroponic plants using the multiple linear regression method. The system only automated the pH of hydroponic nutrient.…”

Section: Introductionmentioning

confidence: 99%

NFT Hydroponic Control Using Mamdani Fuzzy Inference System

Agustian

Prayoga

Santosa

et al. 2022

Journal of Robotics and Control

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The Nutrient Film Technique (NFT) method is one of the most popular hydroponic cultivation methods. This method has advantages such as easier maintenance, faster and optimal plant growth, better use of fertilizers, and less deposition. The disadvantages of NFT include the consumption of electrical power and the faster spread of disease. Therefore, NFT requires a good nutrient control and monitoring system to save electricity and achieve optimal growth and resistance to pests and diseases. In this study, a nutrient control was designed with indicators of pH and TDS levels and equipped with an Internet of Things (IoT) based monitoring system. The control system used is the Mamdani Fuzzy Inference System. The output of the system is the active time of the pH Up, pH Down, and AB Mix nutrient pumps, which aim to normalize the pH and TDS of nutrient liquids. The experimental results show that one to three control steps are needed to normalize pH. One control step has a response time of 60 seconds, and it can prevent pH Up and pH Down oscillations. As for TDS control, the prediction of AB mix pump active time works accurately, and TDS levels can be normalized in one control step. Overall, based on surface control, simulations, and real experimental data, it is indicated that the control system operates very well and can normalize pH and TDS to the desired normal standard.

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Nutrient Solution Acidity Control System on NFT-Based Hydroponic Plants Using Multiple Linear Regression Method

Cited by 10 publications

References 7 publications

The comprehensive application of the lawn ecology of the football field in enhancing the intention of sports consumption

The comprehensive application of the lawn ecology of the football field in enhancing the intention of sports consumption

Automated pH Adjustment Driven by Robotic Workflows and Active Machine Learning

NFT Hydroponic Control Using Mamdani Fuzzy Inference System

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