Control of milk pasteurization process using model predictive approach

YesA milk pasteurization process, a nonlinear process and multivariable interacting system, is difficult to control by the conventional on-off controllers since the on-off controller can handled the temperature profiles for milk and water oscillating over the plant requirements. The multi-variable control approach with model predictive control (MPC) is proposed in this study. The proposed algorithm was tested for control of a milk pasteurization process in three cases of simulation such as set point tracking, model mismatch, difference control and prediction horizons, and time sample. The results for the proposed algorithm show the well performance in keeping both the milk and water temperatures at the desired set points without any oscillation and overshoot and giving less drastic control action compared to the cascade generic model control (GMC) strategy

Minimization of water and chemical usage in the cleaning in place process of a milk pasteurization plant

Cleaning in place (CIP) is a method of cleaning inner surfaces of piping, vessel, equipment, and associated fitting withdisassembly. Although, the CIP processes have been studied continually to improve efficiency for chemical and water consumption,the real conventional plant operations of this process still have been considered as a large amount of consumption.The objectives of this work are to study process behaviors and to find out the optimal draining ratio of the CIP cleaningchemicals in a pasteurized milk plant. To achieve these, mathematical models of the CIP process have been developed andvalidated by the actual process data. With these models, simulation study has been carried out to describe the dynamicbehaviors of the process with respect to the concentrations and contaminations in CIP cleaning chemicals. The optimizationproblem has been formulated and solved using written programs based on MATLAB application program

Real-time experimental implementation of predictive control schemes in a small-scale pasteurization plant

Model predictive control (MPC) is one of the most used optimization-based control strategies for large-scale systems, since this strategy allows to consider a large number of states and multi-objective cost functions in a straightforward way. One of the main issues in the design of multi-objective MPC controllers, which is the tuning of the weights associated to each objective in the cost function, is treated in this work. All the possible combinations of weights within the cost function affect the optimal result in a given Pareto front. Furthermore, when the system has time-varying parameters, e.g., periodic disturbances, the appropriate weight tuning might also vary over time. Moreover, taking into account the computational burden and the selected sampling time in the MPC controller design, the computation time to find a suitable tuning is limited. In this regard, the development of strategies to perform a dynamical tuning in function of the system conditions potentially improves the closed-loop performance. In order to adapt in a dynamical way the weights in the MPC multi-objective cost function, an evolutionary-game approach is proposed. This approach allows to vary the prioritization weights in the proper direction taking as a reference a desired region within the Pareto front. The proper direction for the prioritization is computed by only using the current system values, i.e., the current optimal control action and the measurement of the current states, which establish the system cost function over a certain point in the Pareto front. Finally, some simulations of a multi-objective MPC for a real multi-variable case study show a comparison between the system performance obtained with static and dynamical tuning.Peer ReviewedPostprint (author's final draft

Analysis of RFI Identification and Mitigation in CAROLS Radiometer Data Using a Hardware Spectrum Analyser

A method to identify and mitigate radio frequency interference (RFI) in microwave radiometry based on the use of a spectrum analyzer has been developed. This method has been tested with CAROLS L-band airborne radiometer data that are strongly corrupted by RFI. RFI is a major limiting factor in passive microwave remote sensing interpretation. Although the 1.400–1.427 GHz bandwidth is protected, RFI sources close to these frequencies are still capable of corrupting radiometric measurements. In order to reduce the detrimental effects of RFI on brightness temperature measurements, a new spectrum analyzer has been added to the CAROLS radiometer system. A post processing algorithm is proposed, based on selective filters within the useful bandwidth divided into sub-bands. Two discriminant analyses based on the computation of kurtosis and Euclidian distances have been compared evaluated and validated in order to accurately separate the RF interference from natural signals

Novel economizer for waste heat recovery in pasteurized milk production

noAn economizer, one type of heat exchangers, is specifically named to carry out heat exchange between hot gas and water. It is considered in this work to provide heat recovery between exhaust gas and boiler's feed water. As it has been studied to obtain high heat recovery with several designs, here the economizer is devised with new approach to achieve the best heat recovery as well as economical applicability in an existing pasteurized milk plant. The new economizer is designed to divide an exhaust gas into two portions flowing up on the left and right of the economizes passing across aligned banks of tubes and then flowing down and up again in an unmixed-triple pass fashion. The pressure drop and dew point temperature of corrosive acid depended on the fuel's type are also taken into account. Moreover, the mathematical models based on the energy equation in partial differential equations of two-dimensional initial value problems have been developed to simulate the performance of the newly designed economizer. The observation data prove that the designed economizer can achieve the heat recovery of the exhaust gas up to 57% with the average of 38% and can save the consumption of liquefied petroleum gas of about 13%

A newly designed economizer to improve waste heat recovery: A case study in a pasteurized milk plant

noAn economizer is normally employed to perform heat recovery from hot exhaust gases to cold fluid. In this work, a newly designed economizer is devised to achieve high heat recovery in a pasteurized milk plant. In the economizer, the hot exhaust gas is divided into two channels flowing up on the left and right sides. After that, it is moving down passing over aligned banks of tubes, which water is flowing inside, in a triple passes fashion. Moreover, three dimensional (3D) models with heat transfer including fluid dynamic have been developed, validated by actual plant data and used to evaluate the performance of the economizer. Simulation results indicate that the newly designed economizer can recover the heat loss of 38% and can achieve the cost saving of 13%