The presence of static friction or stiction in a valve can lead to poor performance in industrial automation and control system. This paper presents a method to detect and quantify stiction based on normality test and Hammerstein system identification. Detection and quantification are showed by the f s and f d parameters, where the value of f s and f d greater than 0 indicate the valve suffer from stiction. The proposed method is implemented to a confirmed sets of industrial data as a validation. The result show that the proposed method can accurately detect the presence of stiction and distinguish it to other problem in control loops.

Energy-intensive sectors in emerging nations have the simultaneous difficulties of trying to diminish greenhouse gas emissions while maintaining a stable and cost-effective energy supply. Rooftop solar photovoltaic (PV) systems offer a viable solution, especially in tropical areas like Indonesia that have elevated solar irradiance. This study employs a comprehensive methodology to evaluate the structural, economic, and safety viability of rooftop photovoltaic adoption in the Fast-Moving Consumer Goods (FMCG) sector. Structural analysis utilizing the PMM Ratio verified that industrial rooftops can support a 599 kWp photovoltaic system with minimal reinforcements. The economic assessment revealed substantial feasibility, featuring a Levelized Cost of Energy (LCOE) of Rp 261.40/kWh (about USD 0.016/kWh), yearly savings of Rp 1.36 billion (approximately USD 89,000), a Return on Investment (ROI) of 570%, and a payback duration of 3.73 years. The safety evaluation utilizing the Hazard Identification and Risk evaluation (HIRA) technique found significant hazards—working at height, electrical faults, and fire risks—and recommended mitigation measures in accordance with IEC and Indonesian standards. The findings establish a replicable paradigm for assessing rooftop photovoltaic systems in energy-intensive sectors and furnish actionable recommendations for policymakers and industry executives to expedite the adoption of renewable energy in tropical emerging economies.

Real-time operations of complex algorithms such as Model Predictive Control (MPC), which involves matrix manipulation and solvers, require high processing resources. IEC 61499 may play a significant role in enabling low-cost MPC solutions. This work aims to design an IEC 61499 Function Block (FB) that encapsulates the single variable unconstrained Dynamic Matrix Control (DMC) algorithm and to implement the FB for Fluid Catalytic Cracking (FCC) process control. FB was constructed by transforming the mathematical equations of the DMC algorithm into Basic FBs (BFB) and connecting them to form a Composite Function Block (CFB) using Framework for Industrial Automation & Control (4DIAC), a well-known IEC 61499 software. The designed CFB was implemented in the FCC model IV simulator that controls the reactor pressure, the regenerator-reactor pressure difference, and the total airflow rate to the regenerator. Horizon configuration of the designed DMC CFB is limited. The control horizon criteria are 1≤M≤6, the prediction horizon criteria are P≤100, where P×M is≤200, and the model horizon N is equal to P. The developed CFB demonstrated an adequate performance in fulfilling set points and rejecting disturbance effects. FCC constraints can be well-maintained using the designed FB at normal operating condition and at ambient air temperature change. Nevertheless, reactor temperature constraint was unfulfilled when the feed temperature or the coking factor changes.

Oscillations in the control loops indicate the poor performance of the control loops. The occurrence of oscillations in the process control loop is quite high in the industry, so it needs to be reduced so that the control loop can work properly. The first step for oscillation reduction is oscillation detection. One type of oscillation that is difficult to detect is intermittent oscillation. The smart factory concept encourages the development of the intermittent oscillation detection system using machine learning by being implemented online. Therefore, in this study an online intermittent oscillation detection program is built using K-nearest neighbor (KNN)-based Semi-supervised learning (SSL) method. The SSL method applied is self-training. The training data was obtained by a simulation of the Tennessee Eastman Process. The data is segmented based on window size and extracted time series features. The extracted data is used to build a model to detect oscillations caused by stiction, tuning errors, and external disturbances in the reactor. The model is implemented online with sliding windows and MQTT. The best accuracy and F1-score of the model obtained are 96.15% and 95.15%. In online detection, the model detects the type of oscillation with an average time of 305 seconds.

The oscillation of a process variable is a common indication of poor performance in a chemical plant. It is usually caused by three major problems, i.e. external sinusoid disturbance, mistuned controller, and valve stiction. The problem should be diagnosed to eliminate this oscillation. This paper presents a new method to diagnose the oscillation. This method is based on discrete dyadic wavelet transform combined with fuzzy logic for decision analysis. It is evaluated by oscillating the process of variable data sets. The data sets are simulated in first order plus delay time control loop in five different conditions. The evaluation results show that this method can diagnose the oscillation correctly

The industrial internet-of-things (IIoT) has recently become an important requirement in the process industry. The factories must be able to integrate process automation devices such as programmable logic controllers and industrial computers with mobile devices, especially to support their maintenance and operations. Connectivity with mobile devices has the consequence that cellular networks must be specified to the needs of the industry itself. Comparative studies on using cellular networks in process automation systems are urgently needed. The research that has been conducted is a comparative study between the use of 4G and 5G cellular networks in IIoT process automation systems. It can be seen in the result that the 4G cellular network is sufficient to be used for industries that require mobile devices for monitoring functions, as seen from the results showing the latency obtained is 17.03 ms, jitter is 9.5 ms, packet loss is 6.67 %, and throughput is 192.73 Kbps. However, for the industry that needs to perform real-time control, mobile connectivity has to use a 5G network with better performance metrics with a latency of 15.21 ms, jitter of 5.43 ms, packet loss of 2.67 %, and throughput of 217.19 Kbps. The research results are needed by the process industry in Indonesia, which is widely spread on the island as an archipelago with quite varied cellular network connectivity quality.