A pH process control embedded on a PLC using Fuzzy Logic

Velazquez-Gonzalez, Roberto S.; Gomez-Lemus, T.; Rodríguez‐Reséndiz, Juvenal

doi:10.1109/coniin.2017.7968179

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…For example, at an initial pH value of 9.38, it takes the system approximately 9 min to stabilize its value and reach the pre-established setpoint of 6.45 ( Figure 2). An average pH = 6.455 ± 0.015 is attained in continuous mode, which is of equal magnitude to the final error-value of 0.035 pH units reported in a batch system [26], which is an excellent result for a continuous system where the control is more challenging. reported in a batch system [26], which is an excellent result for a continuous system where the control is more challenging.…”

Section: Ph Controlsupporting

confidence: 55%

Water Treatment Plant Prototype with pH Control Modeled on Fuzzy Logic for Removing Arsenic Using Fe(VI) and Fe(III)

Paredes-Larroca

Saettone-Olschewski

Quino-Favero

et al. 2020

Water

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This study proposes a fuzzy control strategy embedded in a Siemens IoT2040 gateway developed for removing inorganic arsenic from synthetic underground water in a treatment plant prototype. The prototype is used to dose a constant flow of Fe(VI) to maintain an oxide-reduction potential to guarantee the oxidation of arsenite into arsenate, while the fuzzy logic embedded in the IoT control manages the addition of Fe(III) to achieve a proper pH adjustment and efficient arsenate removal. The tests used synthetic Bangladesh groundwater enriched with 200 µg/L of arsenite and 200 µg/L of arsenate. The results revealed that the plant prototype yielded an effective treatment of the water. Arsenate was decreased to an average value of 6.66 µg/L and, the arsenite concentration decreased to 1.01 µg/L or less. These values were lower than the limit of 10 µg/L deemed by the World Health Organization as safe for human consumption.

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Section: Ph Controlsupporting

confidence: 55%

Water Treatment Plant Prototype with pH Control Modeled on Fuzzy Logic for Removing Arsenic Using Fe(VI) and Fe(III)

Paredes-Larroca

Saettone-Olschewski

Quino-Favero

et al. 2020

Water

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“…Penelitian ini menggunakan konsep metode logika Fuzzy Mamdani untuk mengendalikan salinitas air pada sistem budi daya ikan mujair [1]. Input dari kualitas air tambak adalah parameter salinitas yang diubah menjadi dua membership function yaitu variabel error dan variabel delta error [2]. Data tersebut digunakan sebagai input pada mesin inferensi Fuzzy Mamdani.…”

Section: Pendahuluanunclassified

Sistem Wireless Sensor Network untuk Kontrol Salinitas Air Menggunakan Metode Logika Fuzzy Mamdani pada Tambak Ikan Mujair

Yumandana,

Priananda,

Rahayu

2023

JTITS

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Abstrak-Usaha budi daya ikan mujair pada BUMDes Delta Mina Sejahtera memerlukan perhatian terkait salinitas air tambak. Hal tersebut memengaruhi pertumbuhan dan keberlangsungan hidup ikan mujair. Ikan mujair air tawar dapat hidup di perairan air asin dengan proses adaptasi yang bertahap, sehingga untuk membudidayakan di perairan asin perlu dilakukan adaptasi terlebih dahulu secara bertahap sekitar 1-2 minggu. Salinitas digambarkan sebagai ukuran yang menggambarkan tingkat keasinan suatu perairan. Parameter ini dipengaruhi oleh evaporasi pada suatu daerah seperti halnya yang terjadi pada Delta Mina Sejahtera. Nilai salinitas ideal bagi pertumbuhan ikan mujair berkisar 7-8 ppt. Kadar garam yang tinggi dapat diturunkan nilainya dengan memberikan air tawar. Karakteristik tambak yang luas dibutuhkan sistem pengambilan dan pengolahan salinitas yang akurat. Dalam mengatasi permasalahan yang timbul, dibuatkan prototipe sistem kontrol salinitas air dengan mengimplementasikan sistem wireless sensor network (WSN). Penggunaan wireless sensor network bertujuan untuk mendapatkan nilai salinitas yang merata dengan menyebarkan node sensor ke beberapa titik pada area tambak yang luas. Data yang didapat dari node sensor dikirim ke node master dengan menggunakan radio transceiver. Pada node master terdapat kendali salinitas air menggunakan metode logika Fuzzy Mamdani. Pada sistem kontrol yang diimplementasikan, fungsi keanggotaan (membership function) terdiri dari dua input berupa error dan delta error dari pembacaan sensor salinitas yang nantinya menghasilkan output berupa nilai waktu pengiriman air tawar oleh pompa. Berdasarkan hasil pengujian pada prototipe yang telah dibuat, penerapan WSN dalam mengirimkan data dari node sensor ke node master mendapat delay sebesar 2,4 detik. Pada sistem kontrol salinitas menggunakan logika Fuzzy Mamdani, mampu mencapai keadaan salinitas pada setpoint senilai 7,5 ppt dengan error steady state sebesar 0%. Jika dibandingkan dengan hasil simulasi MATLAB, rata-rata respons waktu yang dibutuhkan sistem prototipe untuk mencapai nilai salinitas akhir 7,5 ppt lebih lambat 1,55 detik.

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“…Taking advantage of the internet of things, fuzzy logic is employed to manage the EC and pH parameters of hydroponic systems [14]. Fuzzy logic is incorporated in PLCs for pH process control [15]. The objective of this study is to design a fuzzy logic-based greenhouse irrigation control system..…”

Section: Introductionmentioning

confidence: 99%

Embedded fuzzy logic for controlling pH and nutrition in hydroponic cultivation

Nasution,

Satriyo,

Dhafir

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The uptake of plant nutrients causes nutritional imbalances in hydroponic recirculating systems. Generally, the biological parameters of nutrition solution systems are regulated by pH and nutrients that affect plant health; thus, they must be monitored and managed. In order to solve this issue, a fuzzy logic-based automated control system for intelligent hydroponics was developed. This paper describes the implementation of fuzzy logic controllers into a hydroponic monitoring system. This method produces a controlled hydroponic system in hydroponics by first gathering hydroponic plant data in real time. The system is created using the following steps: system design, prototyping, testing, and assessment. The system’s input consists of TDS and pH sensors, which are used to detect the plant’s nutrient content and pH. The system’s output is a 4-channel relay coupled to 4 small pumps for raising the pH level (pH up), reducing the pH level (pH down), and delivering nutrition AB mix. The fuzzy inference engine employs a combination of the Min-Max and Mamdani techniques. Defuzzification was performed using the centroid technique. The embedded fuzzy logic library (eFLL) is an auxiliary library used by the Arduino IDE. The developed mechanism makes appropriate selections about the nutrition solution management system and pH level.

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A pH process control embedded on a PLC using Fuzzy Logic

Cited by 8 publications

References 3 publications

Water Treatment Plant Prototype with pH Control Modeled on Fuzzy Logic for Removing Arsenic Using Fe(VI) and Fe(III)

Water Treatment Plant Prototype with pH Control Modeled on Fuzzy Logic for Removing Arsenic Using Fe(VI) and Fe(III)

Sistem Wireless Sensor Network untuk Kontrol Salinitas Air Menggunakan Metode Logika Fuzzy Mamdani pada Tambak Ikan Mujair

Embedded fuzzy logic for controlling pH and nutrition in hydroponic cultivation

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