The PID controller is widely used in industries because of its simplicity and robustness. A simple approach to improve regulatory control performance is to combine both feedback PID and feedforward ...controllers. The feedforward controller enables early compensation of a measured disturbance before it can seriously affect the process. The conventionally derived non-ideal feedforward controllers are not often used in practice. The reason is that an ideal feedforward controller based on direct inversion of process model is often not physically realizable. Several non-ideal feedforward control designs have been proposed where some of them involve rather intensive tuning procedure to obtain good disturbance rejection. In this paper, we present a new systematic method for designing a combined feedback-feedforward control system. The proposed design method is easy to use and applicable to stable, unstable and integrating deadtime processes where the ideal feedforward controller is physically not realizable.
The therapeutic effects of antioxidant-loaded nanoemulsion can be often optimized by controlling the release rate in human body. Release kinetic models can be used to predict the release profile of ...antioxidant compounds and allow identification of key parameters that affect the release rate. It is known that one of the critical aspects in establishing a reliable release kinetic model is to understand the underlying release mechanisms. Presently, the underlying release mechanisms of antioxidants from nanoemulsion droplets are not yet fully understood. In this context, this review scrutinized the current formulation strategies to encapsulate antioxidant compounds and provide an outlook into the future of this research area by elucidating possible release mechanisms of antioxidant compounds from nanoemulsion system.
This paper investigates process modelling and reactor design for the reducer in the chemical looping hydrogen production (CLHP) process. The CLHP process adopts a three-reactor technology that can ...provide an efficient and sustainable alternative to the current hydrogen production technology via steam methane reforming (SMR), which suffers from several limitations during industrial operation. CLHP can achieve higher thermal efficiency than SMR and provide a carbon capture and storage (CCS) system. So far, no report on the modelling analysis of the reducer despite its critical dependence on temperature. The modelling study adopts the modified pellet-grain model at the micro-scale and counter-current moving bed model reactor at the reactor level. Simulation results of the gas-solid behavior based on the multi-scale model agree with the literature evidence. Critical information from the model revealed that the oxygen carriers (solids) can attain a desired state, but the syngas remains underutilized. The model simulation further suggests that lowering the gas-solid velocity ratio (Vgs) can substantially promote the syngas conversion. However, the Vgs value must remain above a threshold value (170), defined through the limitation of gas-solid velocities in a moving bed reactor. Since a CCS system requires high purity (>95%) of the product gas, rigorous temperature-pellet size optimization is vital to achieving the target purity while maintaining desired solid state.
Biohydrogen production from renewable resources using dark fermentation has become an increasingly attractive solution in sustainable global energy supply. So far, there has been no report on the ...controllability analysis of biohydrogen production using dark fermentation. Process controllability is a crucial factor determining process feasibility. This paper presents a new criterion for assessing biohydrogen process controllability based on PI control. It proposes the critical loop gain derived via Routh stability analysis as a measure of process controllability. Results show that the dark fermentation using the bacteria from anaerobic dairy sludge and substrate source from sugarcane vinasse can lead to a highly controllable process with a critical loop gain value of 4.3. For the two other cases, an increase of substrate concentration from 10 g/L to 40 g/L substantially reduces the controllability. The proposed controllability criterion is easily adopted to assess the process feasibilty based on experimental data.
Soft sensors are becoming increasingly important in our world today as tools for inferring difficult-to-measure process variables to achieve good operational performance and economic benefits. Recent ...advancement in machine learning provides an opportunity to integrate machine learning models for soft sensing applications, such as Least Square Support Vector Regression (LSSVR) which copes well with nonlinear process data. However, the LSSVR model usually uses the radial basis function (RBF) kernel function for prediction, which has demonstrated its usefulness in numerous applications. Thus, this study extends the use of non-conventional kernel functions in the LSSVR model with a comparative study against widely used partial least square (PLS) and principal component regression (PCR) models, measured with root mean square error (RMSE), mean absolute error (MAE) and error of approximation (E a ) as the performance benchmark. Based on the empirical result from the case study of the penicillin fermentation process, the Ea of the multiquadric kernel (MQ) is lowered by 63.44% as compared to the RBF kernel for the prediction of penicillin concentration. Hence, the MQ kernel LSSVR has outperformed the RBF kernel LSSVR. The study serves as empirical evidence of LSSVR performance as a machine learning model in soft sensing applications and as reference material for further development of non-conventional kernels in LSSVR-based models because many other functions can be used as well in the hope to increase the prediction accuracy.
This research project set out to investigate low salinity water/Methyl Ester Sulphonate (MES) surfactant/nano-silica synergy to enhance oil recovery from sandstone reservoir. A Series of experimental ...works, including contact angle measurements (Sessile drop technique) and UV-vis spectrophotometer tests, were conducted to ascertain the effect of the synergy solution on wettability alteration and surfactant adsorption reduction. Results showed that MES surfactant at 750 ppm and 1000 ppm reversed oil-wet sandstone to a water-wet state. Further reduction was observed at low salinity (250 ppm CaCl 2 ) under high pH conditions. The lowest contact angle measured was 18 degrees with the synergy solution of 750 ppm MES and 250 ppm CaCl 2 at high pH conditions. The maximum adsorption capacity was used as criteria to measure surfactant adsorption loss reduction. It was observed that surfactant adsorption capacity reduced from 4.66 mg/g to 0.85 mg/g when 25 ppm nano-silica was added at 70℃ temperature. This shows that the synergy was able to restore wettability to preferable water-wet conditions to support oil recovery and reduce the excessive loss of surfactant to the sandstone reservoir rock. Water-wet wettability condition and surfactant adsorption reduction are beneficial to the c-EOR project in terms of efficient cost savings on the quantity of surfactant usage for the project. At the same time, overall additional oil recovery is greatly improved.
Precious metals are valuable commodities providing superior protection against risky financial exposure. Identifying factors influencing the market is crucial for anticipating changes. Forecast ...applications utilize stochastic models capable of learning from historical data to project future values. The dataset is a vital component for prediction tools since all estimations begin with constructing the appropriate information. Detecting the association between input and output is essential to filter data, as including unrelated variables could destabilize the response. Feature selection considers removing uncorrelated attributes before incorporating them as inputs to the predictor. This study employs three regression-based algorithms to examine 58 precious assets from gold, silver, platinum, and palladium markets against several variables cited in the literature. Relationships were detected using regressive feature selection methods, known as least absolute shrinkage and selection operator (LASSO), ridge, and elastic net (EN). Results demonstrate that the proposed algorithms achieved satisfactory performance on 42 assets, justified through a reliable fit and acceptable error. The remaining 16 assets exhibited large deviations with considerably poor regression quality, indicating considerable nonlinearity. Attributes were selected with a detailed emphasis on those exerting the most substantial impact on a particular metal. Based on computational analysis, most investments are susceptible to macroeconomic factors. Some assets may present hedging capabilities towards key features, including stock index, exchange rates, and bond yield. An assessment of common variables among each metal revealed that real GDP growth and interest rates are vital indicators for the precious metal market. Overall, the simulation outcomes show no consistent commonalities amongst attributes within the same asset class in a country. Feature selection from this research offers necessary information regarding time-series dynamics, serving as a basis to project trends. The filtered dataset is expected to enhance the reliability of nonlinear predictive algorithms by removing inaccurate correlations to lower computational load. Furthermore, the outcome provides information regarding correlations affecting global precious metal investments over five-year period. These discussions are necessary for investors considering such commodities as potential portfolio diversifiers.
Pollution from industrial effluents and domestic waste are two of the most common sources of environmental pollutants. Due to the rising population and manufacturing industries, large amounts of ...pollutants were produced daily. Therefore, enhancements in wastewater treatment to render treated wastewater and provide effective solutions are essential to return clean and safe water to be reused in the industrial, agricultural, and domestic sectors. Nanotechnology has been proven as an alternative approach to overcoming the existing water pollution issue. Nanoparticles exhibit high aspect ratios, large pore volumes, electrostatic properties, and high specific surfaces, which explains their efficiency in removing pollutants such as dyes, pesticides, heavy metals, oxygen-demanding wastes, and synthetic organic chemicals. Machine learning (ML) is a powerful tool to conduct the model and prediction of the adverse biological and environmental effects of nanoparticles in wastewater treatment. In this review, the application of ML in nanoparticle-treated water on different pollutants has been studied and it was discovered that the removal of the pollutants could be predicted through the mathematical approach which included ML. Further comparison of ML method can be carried out to assess the prediction performance of ML methods on pollutants removal. Moreover, future studies regarding the nanotoxicity, synthesis process, and reusability of nanoparticles are also necessary to take into consideration to safeguard the environment.
•New simultaneous multi-loop PID tuning using 4 parameters for an n×n MIMO process.•Heuristic and optimization procedures are proposed to tune the 4 parameters.•Difficult multi-loop PID tuning is ...reduced to a simple task by using the method.•Numerical examples show the effectiveness of the method.
A multi-loop PID control system is widely used in process industry where finding good values for the controller parameters is very challenging without systematic procedures. In this paper, based on the multi-scale control scheme a simultaneous multi-loop tuning method for an arbitrary n×n MIMO processes is proposed. The salient feature of the method is that it reduces the multi-loop PID tuning parameters from 4n to only 4 common multi-scale control parameters. Both heuristic- and optimization-based procedures are proposed in order to apply the simultaneous tuning method, which should substantially simplify difficult multi-loop tuning involving numerous controller parameters. To ensure adequate robustness of the multi-loop control system, the balanced sensitivity function is used with the tuning method. Numerical studies based on 2×2, 3×3, 4×4 and 5×5 MIMO processes demonstrate the effectiveness of the tuning method.
