Innovation Technology of Engineering Evaporation in the Accelerated Process into Old Brine with an Adaptive Fuzzy Logic Control

Indonesia is an archipelagic country with vast coastal areas and significant potential for salt production, particularly through solar-based evaporation ponds. According to data from the Ministry of Marine Affairs and Fisheries - Republic of Indonesia, national salt production is expected to reach 2.04 million tons in 2024 [1], exceeding the initial target of 2 million tons. However, salt production is still unable to meet domestic demand. This is proven by the data released by the Central Bureau of Statistics, which states that Indonesia still must import approximately 2.8 million tons of salt to fulfill national requirements [2]. The amount is in accordance with the total national demand of around 4.8 million tons. This continued dependence on imported salt highlights the inefficiency of traditional production systems that rely heavily on uncontrolled natural evaporation.

Based on field observations, many obstacles still challenge salt farmers. One of the main issues is the long maturation process of young brine (seawater) into old brine in a traditional pond system. This process heavily depends on weather conditions, natural evaporation rates, and conventional and management practices [3-5]. Consequently, salt production takes a lot of time, results in inconsistent quality, and is vulnerable to environmental fluctuations. These limitations prevent the national salt industry from meeting domestic demand, leading to continued reliance on large-scale salt imports for both industrial and household use.

Research on the acceleration process, especially the transition from young salt water to old salt water, has been relatively scarce, particularly regarding innovative technology. Earlier studies still mainly focus on conventional evaporation methods [6-9]. However, this approach has limitations due to reliance on environmental factors and inefficiency with time. This highlights the need for more adaptive and intelligent control technologies to stabilize the process and enhance energy efficiency.

Refer to Table 1, some can be found:

(1) The evaporation method that has been implemented can contribute to increasing salt production, including accelerating evaporation, increasing °Be, and improving salt content. However, the proposed research will focus on adaptive engineering innovation to accelerate the upgrading of young brine (raw brine) to old brine (20-27 °Be).

(2) Innovation proposed technology is developed from several previous studies on salt evaporation technology.

(3) Based on previously reported research data, there have not been many studies conducted about the acceleration of transforming young brine into old brine. Therefore, this study proposes an intelligent fuzzy logic–based evaporation control system as a novel contribution to salt processing technology in Indonesia.

Table 1. The research art

Although these previous methods achieved improved evaporation rates, none incorporated adaptive control algorithms capable of maintaining optimal thermal and humidity stability during operation.

As a solution to the problems described, an adaptive evaporation control technology is required to accelerate the conversion of young brine into old brine in a shorter time frame. This technology aims to accelerate the improvement in salt water concentration from 2-4 °Be to 20-27 °Be. Naturally, this process usually takes about 30 days (1 month), but with the proposed implementation technology, changes can be expected to accelerate to 2-3 days. This technology replaces the hot sun with a heating-controlled artificial heater. The system applies a Fuzzy Logic Control (FLC) algorithm that manages two key subsystems: (a) the artificial heating element for maintaining the optimal temperature and (b) an exhaust fan–based ventilation system to regulate room humidity dynamically. The hope is that the old salt water from the results will yield appropriate salt meeting industrial specifications or known industrial salt.

The technology being researched aligns with the President's Asta Cita Republic of Indonesia, which emphasizes improving the productivity of the maritime sector and the independent economy, particularly in the salt industry sector, which has so far relied on imports. This research also supports the achievement of the Sustainable Development Goals (SDGs), in particular SDG 2–Zero Hunger, by increasing the production of salt, a material essential to the food industry and fisheries. This research also supports SDG 9–Industry, Innovation, and Infrastructure, by implementing innovative technology in process manufacturing to Power local sources. Besides that, heating integration electrically controlled by fuzzy logic also contributes to SDG 13–Climate Action, by reducing dependence on methods that affect natural evaporation due to climate change, allowing for more consistent and sustainable salt production. Hence, this study provides a sustainable, scalable approach to modernizing Indonesia’s salt production process through intelligent and adaptive control integration.

3.1 FLC approach

In this study, the FLC approach was used. Because this approach is capable of handling uncertainty [20, 21], nonlinearity [22], as well as complex processes difficult to model with conventional mathematical methods [23-26]. The process maturation of old brine involves numerous dynamic variables, such as temperature, air, humidity, water temperature, and salt level (PPM). The relationship between these parameters is not always naturally linear, so that system-based logic, binary or conventional PID control, is less optimal for adapting to changing environmental conditions in real time [27-30]. As for details, the FLC design for this research is as follows:

1. Fuzzification

In this part, a variable is presented as a fuzzy set to handle uncertainty in the process change environment.

Function membership for the arrangement speed blower.

• Variables Input

(1) Air temperature is categorized into five levels: Very Low (SR), Low (R), Medium (S), High (T), and Very High (SD) categories. The details of the variables, input temperature, and air, are explained in Figure 1.

(2) Air humidity is divided into the following categories: Very Dry (SK), Dry (K), Medium (S), Moist (L), and Very Humid (SL). The detailed variables for input humidity air are explained in Figure 2.

Figure 1. Variables input air temperature

Figure 2. Variables input air humidity

•  Variables Output

The speed blower consists of five categories: Very Slow (SL), Slow (L), Medium (S), Fast (C), and Very Fast (SC). In detail, the variables related to the blower’s output speed are explained in Figure 3.

Figure 3. Variables output speed blower

Function membership for the arrangement temperature heating artificial (heater).

• Variables Input

(1) Salt temperature is divided into the following categories: Very Cold (SD), Cold (D), Normal (N), Hot (P), and Very Hot (SP). Details of the variables, including input salt temperature, are explained in Figure 4.

(2) Salinity (PPM) consists of Unsaturated (TJ), Moderately Saturated (JS), Saturated (J), Very Saturated (SJ), and Perfect (SP) categories. Details of the variables, including input temperature and air, are explained in Figure 5.

Figure 4. Variables input salt temperature

Figure 5. Variables input salinity

• Variables Output

The dimmer heater consists of five categories: Very Low (SR), Low (R), Medium (S), High (T), and Very High (ST). Details of the variables and the blower’s output speed are explained in Figure 6.

Figure 6. Variables output dimmer heater

2. Inference

Inference is used to integrate the input and output based on the predefined fuzzy rules (rule base). The inference process aims to make a decision based on a condition that a system is not definite, non-linear, or complex, modeled in a mathematical way. The inference arrangement speed blower can be seen in Table 2, whereas the inference for arrangement heating artificial (heater) can be seen in Table 3.

Table 2. Inference for the arrangement speed blower

2.png	SR	R	S	T	ST
SK	SL	SL	R	R	S
K	SL	R	R	S	S
S	R	R	S	C	C
L	R	S	C	C	SC
SL	S	C	C	SC	SC

Table 3. Inference for the arrangement temperature heating artificial (heater)

3.png	Elementary School	D	N	P	SP
TJ	ST	ST	T	S	R
JS	ST	T	S	S	R
J	T	S	S	R	R
SJ	S	S	R	R	SL
SP	S	R	R	SL	SL

3. Defuzzification

In this research, the defuzzification method used is the Centroid of Area (CoA). This method calculates the point-center area below the curve's fuzzy membership function to produce a mark that is crispier, more stable, and more realistic [14, 15].

Formula of CoA:

$y^*=\frac{\sum_j y_j \mu_c^{a g g r}\left(y_j\right)}{\sum_j \mu_c^{a g g r}\left(y_j\right)}$    (10)

Superiority of FLC in this research lies in its ability to adapt to changing environments, where FLC can overcome fluctuations in temperature and humidity that influence evaporation rates. Control is adaptive based on basically one's own good ability in processing annoying situations with the rules that have been made, and will provide positive feedback if it goes beyond the specified fuzzy rules [31, 32]. With designed fuzzy rules, such as those in point b (rulebase), the system will be able to adapt the speed of the blower and the Power heater in real time, without needing a complicated mathematical model. Additionally, FLC increases energy efficiency by controlling the blower and heater only at the required level, thereby reducing wasted electricity compared to systems with ON/OFF or PID control.

3.2 Design system

In a way, general design technology engineering evaporation is something design mechanics are equipped with for a tub saltwater container. Salt water container is shared into three parts, namely (1) Young brine reservoir (2–4 °Be), (2) Brine reservoir on process second (17–19 °Be), and (3) Receptacle end (Salt water industrially viable). Details can be seen in Figure 7; meanwhile, the optimal design for sensor placement to detect in a way that is good and optimal is shown in Figure 8. The design placement actuator is shown in Figure 9.

Figure 7. Framework design technology engineering evaporation

Figure 8. Sensor placement design

Figure 9. Placement design actuator

Referring to the design placement sensor and actuator in Figures 8 and 9. Referring to the proposed system architecture, the complete electronic design of the adaptive engineering evaporation system, including the sensor interfaces, microcontroller unit, heater control module, and blower driver circuit, is illustrated in Figure 10.

Figure 10. Design electronics technology engineering evaporation

3.3 Fuzzy logic flowchart system

The fuzzy logic flowchart illustrated in Figure 11 describes the overall operational process of the adaptive control system used in the artificial saltwater evaporation chamber, where the sequence begins with the input acquisition of temperature, humidity, and salinity parameters from sensors as crisp data, which are then processed in the fuzzification stage to convert them into linguistic variables representing environmental conditions, followed by the inference and rule-base evaluation process that determines the appropriate control decision based on predefined IF–THEN fuzzy rules, and subsequently the defuzzification stage that transforms the fuzzy outputs into precise control signals in the form of PWM values for the heater and ventilation actuators, enabling the system to adaptively regulate chamber temperature and humidity in real time; if the measured values have not yet met the desired concentration range (20–27 °Be), the process returns to the input stage through a closed-loop feedback mechanism, thereby ensuring that the evaporation process remains stable, energy-efficient, and capable of achieving mature brine concentration within 48 hours.

Figure 11. Flowchart system FLC

The results of this study demonstrate that the adaptive FLC system effectively accelerated the evaporation process, transforming young brine (2–4 °Be) into mature brine (25–27 °Be) within 48 hours, representing a 93% reduction in processing time compared to the conventional solar pond method, which typically requires up to 30 days.

This improvement validates FLC’s ability to maintain stable thermal and humidity conditions through real-time adaptive feedback.

The superior performance of the FLC system arises from its ability to manage nonlinear interactions between temperature, humidity, and salinity, allowing it to balance heat input and ventilation continuously.

Unlike traditional ON/OFF or PID systems, the fuzzy inference mechanism enables smooth actuator transitions and energy-efficient heating control, thereby preventing temperature overshoot and ensuring consistent brine concentration quality.

This research was conducted on a small-scale laboratory prototype and has not yet been tested under outdoor environmental variations.

Therefore, further studies are needed to examine long-term performance, sensor calibration stability, and system robustness under fluctuating ambient conditions such as solar radiation, wind, and humidity.

Future development will focus on integrating solar or hybrid photovoltaic–thermal (PV–T) energy systems to enhance sustainability, as well as on implementing predictive control approaches, such as Model Predictive Control (MPC) or Neuro-fuzzy hybrid models, to improve accuracy and adaptability in large-scale applications further.

The integration of adaptive fuzzy logic in salt evaporation engineering provides a strategic technological solution for the national salt industry, promoting process automation, energy efficiency, and independence from climatic constraints.

This innovation directly supports SDG and SDG 13 by advancing sustainable, intelligent process engineering aligned with renewable energy transition goals.