Optimizing Model Base Predictive Control for Combustion Boiler Process at High Model Uncertainty

Osman, Haitham

doi:10.15255/cabeq.2016.867

Cited by 6 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In industry, the most advanced process control system requires accurate models if high performance is to be attained. Most chemical processes are nonlinear in nature, which makes developing precise models challenging [21,22].…”

Section: Introductionmentioning

confidence: 99%

“…The need for a model that can accurately predict experimental behaviour has been the utmost challenge for researchers over the years; such models can dramatically reduce the time and operational cost in many engineering aspects. From here emerged the need to model sesame seed extraction using various solvents and under different operating conditions [21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents

Osman

Shigidi

Arabi

2019

Foods

View full text Add to dashboard Cite

This paper compares four different modeling techniques: Response Surface Method (RSM), Linear Radial Basis Functions (LRBF), Quadratic Radial Basis Functions (QRBF), and Artificial Neural Network (ANN). The models were tested by monitoring their performance in predicting the optimum operating conditions for Sesame seed oil extraction yields. Experimental data using three different solvents—hexane, chloroform, and acetone—with varying ratios of solvents to seeds, all under different temperatures, rotational speeds, and mixing times, were modeled by the three proposed techniques. Efficiency for model predictions was examined by monitoring error value performance indicators (R2, R2adj, and RMSE). Results showed that the applied modeling techniques gave good agreements with experimental data regardless of the efficiency of the solvents in oil extraction. On the other hand, the ANN model consistently performed more accurate predictions with all tested solvents under all different operating conditions. This consistency is demonstrated by the higher values of R2 and R2adj ratio equals to one and the very low value of error of RMSE (2.23 × 10−3 to 3.70 × 10−7), thus concluding that ANN possesses a universal ability to approximate nonlinear systems in comparison to other models.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents

Osman

Shigidi

Arabi

2019

Foods

View full text Add to dashboard Cite

show abstract

“…In the process industry, if high efficiency is to be achieved, the most advanced process control system needs precise models. Most chemical processes are nonlinear in nature, making the development of precise models difficult [20,21]. Different factors, ranging from model nonlinearity to dimensionality and the data sampling method to internal parameters are considerably influenced when examining the accuracy of the modeling technique [22].…”

Section: Introductionmentioning

confidence: 99%

Model Prediction and Optimization of Waste Lube Oil Treated with Natural Clay

Osman

2019

Processes

View full text Add to dashboard Cite

In this work, used lube oil was treated using natural acid-free clay. Clay was added at different amounts (5, 10, and 20 g) to 100 mL of waste engine oil at various temperatures (250, 350, 400, and 450 °C) and mixed at a speed of 800 rpm for 30 min. After settling and separation, the treated oil was diluted with kerosene before being examined using a Ultraviolet–visible (UV) spectrophotometer. In order to achieve cost-effective recycling, this process is modeled using the response surface method (RSM). Five regression models (linear, quadratic, Two Factor Interactions (2FI), cubic, and reduced-order quadratic model) were developed, then tested, and examined by calculating the statistical performance indicators (R2, R2adj, Akaike’s Information Criterion corrected (AICc), Bayesian Information Criterion (BIC), and Root Mean Square Error (RMSE)). The results obtained reveal that the modified quadratic model outperforms the rest of the models in terms of the low value of RMSE, the lowest AICc, lowest BIC, and the highest R2 and R2adj. The developed modified quadratic model is optimized successfully to predict optimum operation conditions. Results show that optimum operation conditions are at the minimum area under the curve for UV absorption at 223.358; this can be achieved with a process temperature of 266.246 °C and clay quantity of 5.331 g. This model agreed with experimental data regardless of the effectiveness of red clay in the therapy of lube oil.

show abstract

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Dutta

Upreti

2021

Can J Chem Eng

View full text Add to dashboard Cite

In the last three decades, artificial intelligence (AI) has been increasingly and vigorously utilized for process control in chemical, biochemical, and biomedical engineering. These disciplines frequently involve fairly sophisticated processes under risks of operational upsets, and thus have an ever increasing demand of superior control strategies. As the deployment of AI in process control pushes the limits in this regard, the research advances, which are multitudinous and varied, need to be assimilated and organized to help promote further utilization and progress in the field. To that end, we examine more than 280 relevant research publications, and systematically collate the information. The AI‐based technologies are classified, and their over‐arching control paradigm is presented. Common AI‐based control technologies are then presented, which are based on expert systems, fuzzy logic, artificial neural networks, nature‐inspired algorithms, and hybrid approaches. Their working principles, types, and implementations are summarized along with advantages, limitations, and comparisons, if available. Important applications in the above disciplines are included with the help of tables that capture important details. A discussion is also provided on advanced and newly emerging AI‐based control technologies with pertinent applications. Overall trends are analyzed, and future prospects are identified based on the survey.

show abstract

Optimizing Model Base Predictive Control for Combustion Boiler Process at High Model Uncertainty

Cited by 6 publications

References 17 publications

Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents

Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents

Model Prediction and Optimization of Waste Lube Oil Treated with Natural Clay

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Contact Info

Product

Resources

About