Optimal operation of combined heat and power systems: an optimization-based control strategy

The use of decentralized Combined Heat and Power (CHP) plants is increasing since the high levels of efficiency they can achieve. Thus, to determine the optimal operation of these systems in dynamic energy-market scenarios, operational constraints and the time-varying price profiles for both electricity and the required resources should be taken into account. In order to maximize the profit during the operation of the CHP plant, this paper proposes an optimization-based controller designed according to the Economic Model Predictive Control (EMPC) approach, which uses a non-constant time step along the prediction horizon to get a shorter step size at the beginning of that horizon while a lower resolution for the far instants. Besides, a softening of related constraints to meet the market requirements related to the sale of electric power to the grid point is proposed. Simulation results show that the computational burden to solve optimization problems in real time is reduced while minimizing operational costs and satisfying the market constraints. The proposed controller is developed based on a real CHP plant installed at the ETA research factory in Darmstadt, Germany.Peer ReviewedPostprint (author's final draft

Development and Implementation of an Online Kraft Black Liquor Viscosity Soft Sensor

The recovery and recycling of the spent chemicals from the kraft pulping process are economically and environmentally essential in an integrated kraft pulp and paper mill. The recovery process can be optimised by firing high-solids black liquor in the recovery boiler. Unfortunately, due to a corresponding increase in the liquor viscosity, in many mills, black liquor is fired at reduced solids concentration to avoid possible rheological problems. Online measurement, monitoring and control of the liquor viscosity are deemed essential for the recovery boiler optimization. However, in most mills, including those in New Zealand, black liquor viscosity is not routinely measured. Four batches of black liquors having solids concentrations ranging between 47 % and 70 % and different residual alkali (RA) contents were obtained from Carter Holt Harvey Pulp and Paper (CHHP&P), Kinleith mill, New Zealand. Weak black liquor samples were obtained by diluting the concentrated samples with deionised water. The viscosities of the samples at solids concentrations ranging from 0 to 70 % were measured using open-cup rotational viscometers at temperatures ranging from 0 to 115 oC and shear rates between 10 and 2000 s-1. The effect of post-pulping process, liquor heat treatment (LHT) on the liquors’ viscosities was investigated in an autoclave at a temperature >=180 oC for at least 15 mins. The samples exhibit both Newtonian and non-Newtonian behaviours depending on temperature and solids concentration; the onsets of these behaviours are liquor-dependent. In conformity with the literature data, at high solids concentrations (> 50 %) and low temperatures, they exhibit shear-thinning behaviour with or without thixotropy but the shear-thinning/thixotropic characteristics disappear at high temperatures (>= 80 oC). Generally, when the apparent viscosities of the liquors are 50 %, viscosity decreases with increasing RA content of the liquor. This shows that the RA content of black liquor can be manipulated to control the viscosity of high-solids black liquors. The LHT process had negligible effect on the low-solids liquor viscosity but led to a significant and permanent reduction of the high-solids liquor viscosity by a factor of at least 6. Therefore, the incorporation of a LHT process into an existing kraft recovery process can help to obtain the benefits of high-solids liquor firing without a concern for the attending rheological problems. A variety of the existing and proposed viscosity models using the traditional regression modelling tools and an artificial neural network (ANN) paradigm were obtained under different constraints. Hitherto, the existing models rely on the traditional regression tools and they were mostly applicable to limited ranges of process conditions. On the one hand, composition-dependent models were obtained as a direct function of solids concentration and temperature, or solids concentration, temperature and shear rate; the relationships between these variables and the liquor viscosity are straight forward. The ANN-based models developed in this work were found to be superior to the traditional models in terms of accuracy, generalization capability and their applicability to a wide range of process conditions. If the parameters of the resulting ANN models can be successfully correlated with the liquor composition, the models would be suitable for online application. Unfortunately, black liquor viscosity depends on its composition in a complex manner; the direct correlation of its model parameters with the liquor composition is not yet a straight forward issue. On the other hand, for the first time in the Australasia, the limitations of the composition-dependent models were addressed using centrifugal pump performance parameters, which are easy to measure online. A variety of centrifugal pump-based models were developed based on the estimated data obtained via the Hydraulic Institute viscosity correction method. This is opposed to the traditional approaches, which depend largely on actual experimental data that could be difficult and expensive to obtain. The resulting age-independent centrifugal pump-based model was implemented online as a black liquor viscosity soft sensor at the number 5 recovery boiler at the CHHP&P, Kinleith mill, New Zealand where its performance was evaluated. The results confirm its ability to effectively account for variations in the liquor composition. Furthermore, it was able to give robust viscosity estimates in the presence of the changing pump’s operating point. Therefore, it is concluded that this study opens a new and an effective way for kraft black liquor viscosity sensor development

Setpoint Tracking Predictive Control in Chemical Processes Based on System Identification

A Kraft recovery boiler in a pulp-paper mill provides a means for recovery of the heat energy in spent liquor and recovery of inorganic chemicals while controlling emissions. These processes are carried out in a combined chemical recovery unit and steam boiler that is fired with concentrated black liquor and which produces molten smelt. Since the recovery boiler is considered to be an essential part of the pulp-paper mill in terms of energy resources, the performance of the recovery boiler has to be controlled to achieve the highest efficiency under unexpected disturbances. This dissertation presents a new approach for combining system identification technique with predictive control strategy. System identification is the process of building mathematical models of dynamical systems based on the available input and output data from the system. Predictive control is a strategy where the current control action is based upon a prediction of the system response at some number of time steps into the future. A new algorithm uses an i-step-ahead predictor integrated with the least-square technique to build the new control law. Based on the receding horizon predictive control approach, the tracking predictive control law is achieved and performs successfully on the recovery boiler of the pulp-paper mill. This predictive controller is designed from ARX coefficients that are computed directly from input and output data. The character of this controller is governed by two parameters. One parameter is the prediction horizon as in traditional predictive control and the other parameter is the order of the ARX model. A recursive version of the developed algorithm can be evolved for real-time implementation. It includes adaptive tuning of these two design parameters for optimal performance. The new predictive control is proven to be a significant improvement compared to a conventional PID controller, especially when the system is subjected to noise and disturbances

Modeling mass transfer and chemical reaction in industrial nitrocellulose manufacturing processes

A series of models are proposed to describe the production of military grade nitrocellulose from dense cellulose materials in mixtures of nitric acid, sulfuric acid, and water. This effort is conducted to provide a predictive capability for analyzing the rate and extent of reaction achieved under a range of reaction conditions used in the industrial nitrocellulose manufacturing process for sheeted cellulose materials. Because this capability does not presently exist, nitrocellulose producers have historically relied on a very narrow range of cellulose raw materials and resorted to trial and error methods to develop processing conditions for new materials. This tool enables nitrocellulose manufacturers to rapidly adapt to changing market conditions, supply disruptions, or normal variation in the quality of cellulose raw materials and provides process engineers with an improved capability for process control and analysis. This work includes measurement of the kinetics of nitration for cellulose fibers in mixed acids, an evaluation of simultaneous mass transfer and swelling in slivers cut from sheeted cellulose materials, and a structural analysis of slivers cut on industrial rotary cutting machines to consider features that may increase the reactivity of these materials. The kinetics of nitration of all high purity cellulose fibers are demonstrated to be equivalent, and the nitration of dense cellulose materials is shown to be a mass transfer limited process except in the case of small wood pulp slivers in mixed acids used in the production of Grade B nitrocellulose. In addition, it is shown that diffusion and unidirectional swelling occur on similar timescales during the nitration of slivers cut from sheeted wood pulp, resulting in variable diffusivity of mixed acids through the wood pulp sliver structure during the nitration reaction. Finally, delaminated regions or galleries that are formed as a result of the shearing action of the rotary cutting machine used in the industrial nitrocellulose manufacturing process are observed, and the influence of these structural features on the reactivity of the resulting slivers is considered. Based on these findings, generalized models are proposed that can be used to identify optimal processing conditions for new cellulose raw materials to ensure that the resulting nitrocellulose meets quality specifications while avoiding the costs and delays associated with trial and error experimentation

Fuel gas blending benchmark for economic performance evaluation of advanced control and state estimation

A simulation of a fuel gas blending process and its measurement system is proposed as a benchmark test case for advanced control and state estimation. The simulation represents an industrial facility and employs a well-established software environment. The objective is to maintain four controlled variables within specified bounds while minimizing an economic performance index. The controlled variables are the fuel gas pressure and three measures of gas quality. Six feed gas flow rates may be adjusted to achieve the objective. Each has a limited availability. The benchmark consists of three reproducible scenarios, each a 46-h period during which 23 discrete upsets occur and the feed gas compositions vary gradually with time. A benchmark multi-loop feedforward–feedback structure is described, tested, and compared to an estimate of optimal performance. The operating cost provided by the benchmark controller is from 1.19 to 1.71 times higher than the estimated minimum. Readers are challenged to download the simulation model, benchmark controller and estimated optimal performance from the URL given in this paper, and to devise case studies of advanced state estimation and control strategies to better the proposed benchmark controller.http://www.elsevier.com/locate/jprocontai201

CHARACTERIZING AND PREDICTING THE ANTIMICROBIAL PROPERTIES OF LIGNIN DERIVATIVES

Due to the overuse of antibiotics in our society, there has been a steady rise in highly antimicrobial-resistant bacteria in the last decade. This has created a renewed interest in natural phenolic compounds for antimicrobial discovery amongst the scientific community. To this end, lignin is the most abundant naturally occurring phenolic polymer on earth and has already been known to have antimicrobial properties due to its polyphenolic structure. In addition, lignin is considered a major waste product for lignocellulosic biorefineries, and its valorization into value-added products will generate extra profit for a biorefinery, making biofuels less expensive, increasing their marketability as an alternative to fossil fuels. However, the retention of lignin’s antimicrobial properties in different materials, as depolymerized products, or even the prediction of their antimicrobial properties is not well understood in the literature. Much work has utilized lignin as a functional polymer in a variety of composites and materials, but their antimicrobial properties have not been as widely explored. Therefore, ionic liquids were used in the facile preparation of cellulose-based hydrogels, and the addition of different lignocellulosic components (lignin and xylan) or the use of whole biomass (poplar and sorghum) were evaluated for their effects on hydrogel properties (mechanical and antimicrobial). The addition of both lignin and xylan improved hydrogel mechanical strength/stiffness, and lignin-containing hydrogels showed retained antimicrobial properties when screened against the target organism (Escherichia coli). Utilizing raw biomass provided increased mechanical strength (poplar), similar water retention abilities (poplar and sorghum), and retained antimicrobial properties (poplar). These results indicate that the different components of lignocellulose can be used to fine tune the properties of cellulose-based hydrogels and that lignin can confer its antimicrobial properties when incorporated into hydrogels. The antimicrobial properties of different lignin depolymerization products were explored using a reductive and oxidative depolymerization method to produce phenolic rich lignin-based bio-oils. Purified alkali-enzymatic corn stover lignin (AEL) was depolymerized by catalytic transfer hydrogenolysis using supercritical ethanol and a Ru/C catalyst, generating a bio-oil stream at high yields. Sequential extraction was used to fractionate the bio-oil into five fractions with different phenolic compositions using hexane, petroleum ether, chloroform, and ethyl acetate. Antimicrobial properties of the bio-oils were screened against Gram-positive/negative bacteria and yeast by examining microbial growth inhibition. The monomers in the bio-oil fractions contained primarily alkylated phenols, hydrogenated hydroxycinnamic acid derivatives, syringol and guaiacol-type lignins created from reductive cleavages of ether linkages. After sequential extraction, the lignin derived compounds were fractionated into groups depending on solvent polarity. Results suggest that the total monomer concentration and the presence of specific monomers (i.e., syringyl propane) may correlate to the antimicrobial activity of lignin depolymerization products, but the exact mode of action or antimicrobial activity caused by the complex mixtures of monomers and unidentified oligomers remains unclear. The same AEL lignin was depolymerized through oxidative procedures using peracetic acid, and its applications as an antibiotic replacement in the fuel ethanol industry were explored. The resulting bio-oil had a low degree of depolymerization that mostly produced unidentifiable lignin oligomers. Nonetheless, this bio-oil displayed highly selective antimicrobial properties, with up to 90% inhibition of commercially sampled lactic acid bacteria (LAB) at 4 mg/ml and no inhibition of yeast. Using the bio-oil (4 mg/ml) as an alternative antibiotic treatment during simultaneous-saccharification and fermentation of raw corn starch showed an 8% increase in ethanol production at a yeast to LAB ratio of 1:100, compared to untreated contaminated controls. The ability of the bio-oil to improve ethanol yields clearly shows its efficacy as an alternative antibiotic and that depending on depolymerization method lignin derivates can display a variety of useful antimicrobial properties/applications. The final study was the first attempt in the literature to predict the antimicrobial properties of lignin derivatives using quantitative structure−activity relationship (QSAR) models. First, the open-access database ChEMBL, with non-lignin specific compounds, was used to create datasets of compounds with MIC activity measurements against both B. subtilis and E. coli. Machine learning algorithms were used to develop the QSARs for the large ChEMBL datasets and were found to underpredict the antimicrobial activity of actual lignin compounds. Conversely, as metanalysis of the literature containing MIC data of lignin derivatives were used to build QSAR models with ordinary least square regressions (OLS). An accurate QSAR model for E. coli was not found, but a satisfactory model was obtained for the B. subtilis metanalysis dataset. Molecular Operation Environment (MOE)-type descriptors and the number of aliphatic carboxylic acid groups showed strong correlations to the MIC values (R2 of 0.759). Comparatively, an additional dataset was experimentally derived by screening 25 lignin monomers and three dimers against B. subtilis by measuring bacterial load difference (BLD). This datasets QSAR, using OLS, found that MOE-type descriptors and the number of aromatic hydroxyl groups were better predictors of BLD (R2 of 0.831). Thus, the smaller datasets highlighted how the variability in antimicrobial measurements and the specific compounds used will impact the predictive nature of the resulting QSARs. Overall, this entire work provides critical knowledge and guidance on using lignin as an antimicrobial source in different industrial processes/products and the identification of lignin derivatives with enhanced activity

Design Optimization of Highly Uncertain Processes: Applications to Papermaking System

In process design, the goal is to find a process structure that satisfies the desired targets and constraints. A typical task involves decision making related to the process flow-sheet and equipment. This dissertation examines design optimization of papermaking process. The main emphasis is on the development of an optimal design procedure for highly uncertain processes with non-Gaussian uncertainties. The design problem is studied as a multiobjective task in which the most effective process structure is sought by maximizing the process long-term performance and minimizing the investment cost. As the assessment of the long-term performance requires that the process be operated optimally, the optimization of the process operation is studied as a subtask of the design problem. Paper manufacturing is a complex process in which paper is produced from wood, water, and chemicals. The task is to manufacture uniform quality paper while minimizing the costs. If the paper web breaks, all the production is discarded. The unpredictable web breaks strongly disturb the paper production. As a result, the process has two separate operating points: normal operation and operation during web breaks. That poses challenges to the process operation as the transition between the operating points is somewhat random and the future evolution of the process is not completely predictable. In model-based process optimization, the uncertainty related to the models affects the reliability of the results. The modelling uncertainty is associated with both the incom-plete understanding of the process and the approximation due to computational reasons. In papermaking, the unpredictable web breaks are the largest source of uncertainty, but incomplete understanding is also related to e.g. the quality models of the paper. Besides modelling uncertainty, also the uncertainty about the available information, i.e. the measurement accuracy, affects the reliability of the optimization. In this thesis, schedul-ing of the measurement resources is studied as a part of the process optimization. This dissertation proposes a procedure to systematically optimize the design and operation of a papermaking process. The procedure is presented at six stages, including problem formulation, modelling, operational optimization, design optimization, robustness analysis, and validation. The main focus is at the operational and design optimization stages, but the purpose of all stages is discussed. The proposed procedure is demonstrated with case studies. The studied cases deal with two types of problems: discrete state systems with uncertain state information and continuous state systems with two operating points. In both groups, non-Gaussian uncertainty plays an important role

Model-based optimization of a CompactCooking G2 digesting process stage

A CompactCooking™ G2 (Valmet) digesting system represents a challenging process stage to be optimized in the context of a kraft pulp mill. Its highly non-linear behavior due to liquor recycling and heat integration poses a barrier to traditional trial-and-error optimization conducted by physical lab-scale simulation. Hence, this thesis aims to design a solution based on numerical simulation and mathematical optimization, whose results can be directly applied on industrial-scale as computed optimal set-points for the supervisory control. Based on published, first-principles, pulp digester models, a customized dynamic model was developed in Matlab/Simulink to simulate a complete CompactCooking™ G2 stage. The process model is founded on Purdue wood reaction kinetics and Härkönen chips bed compaction models, and it seamlessly takes into account process characteristics mentioned above. The non-linear model was validated by comparison against historical data of an industrial unit (200 h), and then employed in the design of a steady-state optimizer for this process stage by means of linear programming. Simulation results showed very good agreement in terms of liquors residual alkali, weak black liquor solids, and blowline kappa, despite high uncertainty on disturbances data and model simplifications. However, simulated kappa showed higher sensitivity to temperature fluctuations than the plant signal, likely indicating the need for more detail when modelling heat transfer phenomena. As to the optimization goal, a base case scenario (plant steady-state) was identified from industrial data to attempt process economics optimization. The results showed a potential for increasing profit or reducing variable costs in at least 2 USD/ADt, which for a modern pulp mill represents annual benefits between 1 – 2 million USD depending on production rate and mill availability. Further, the simulation model showed remarkable results when used in a novel process analysis technique, called here simulated contribution, letting to explain the variability of blowline kappa in terms of multiple-time-scale process dynamics. In conclusion, a model-based optimization method has been successfully designed for the CompactCooking™ G2 system, and potential economic benefits should encourage industrial testing and further work to develop a real-time optimizer software technology

Intelligent Monitoring of Advanced Control and Optimization

An optimal performance of process controllers and control loops is essential for process economy as well as process quality. The increased cost of energy and raw material as well as customer demand for quality requirements are forcing the control engineers to develop and provide solutions, which can operate in ever changing process conditions cost efficiently without compromising safety. Based on statistics, only a fraction of used control loops are performing at optimum level. In a multivariate process there can be dozens control loops to be monitored, which makes manual inspection difficult. Therefore, a system that automatically evaluates the process state and helps predicting future outcomes using real time optimization and offline data analysis is in order. A control loop performance monitoring system is often used as a support for control optimization. It can also be used for inspection of process actuator condition. A process performance monitoring tools usually makes use of statistical and mathematical methods with a visual user interface to provide adequate amount of data. In this thesis, two process performance monitoring tools for advanced control and opti-mization were implemented. The tools are used to monitor selected control methods, providing essential information about their status. The usefulness of a process perfor-mance monitoring system is demonstrated at a site using real process data. The tools were included into an existing process monitoring system that was already in place at a process site