This paper compares methods of controlling brown stock washing flows during production rate changes. Wash water flow to a brown stock washing system affects both pulp washing loss and black liquor evaporation demand. The complexity of brown stock washing systems has increased with the application of new technology, the increasing demands for improved washing and the inclusion of oxygen delignification systems. Storage and mixing tanks create process lags and dead-times which need to be considered when controlling wash water flows. An optimum operating point exists, which corresponds to the minimum of the combined costs associated with washing loss and evaporation. To ensure operation is sustained as close to this optimum point as possible, a system requires stable control of stock flows, tank levels, feed-forward dilution factor control, as well as feedback control based on washing loss measurements. Sufficient filtrate surge capacity is recommended to balance accumulations in pulp storage tanks, especially during production rate changes. The impacts of production rate disturbances and the consequences of specific control strategies are demonstrated with dynamic simulations of a brown stock washing system. Various methods of controlling inter-stage filtrate flows are compared. The methods studied range from simple feed-forward PID control, to more complex filtrate management strategies including multi-variable fuzzy logic control.

Comments:

This paper (and the sample model washfilt.dra found in the demo software folder Samples > Control) will allow you to evaluate the substantial savings that can be achieved by implementing advanced process control strategies for Brownstock washing to cope with production rate changes.

For the past few years, the pulp and paper industry has faced the difficult challenge of doing more with less, and commercial simulation has become a valuable tool for this purpose.  Doing “more” often means optimizing the process, which in turn means finding combinations of process parameter-values that yield optimum measures of process performance, with the least sensitivity to process disturbances.  In the context of most current commercial simulators, this view of optimization presents two important problems to be solved.  First, the category of process variables often used as qualitative metrics in process performance evaluation and optimization are usually absent from commercial simulator models, i.e. paper strength or pulp color do not lend themselves to mass and energy balances.  Second, optimization is often performed manually by “playing” with the simulator using trial and error variables combinations, i.e. most simulators do not make use of mathematically robust optimization procedures that search for the optimal combination of process variables automatically and systematically. In a previous article we have presented a potential solution to the first problem sited herein by developing and implementing a neural network-based module that can perform independently of the classic heat and material balance of process variables upon which the model is based [5].  In this paper, we tackle the second problem of optimizing some important process quality metrics appearing in new models by systematically searching the “space” of process parameter values that yield their maximum or minimum.  A simulated annealing process, which demonstrates the well-known downhill simplex method was used for this purpose and is described in this paper.  An example simulation that uses a newly developed optimization module is presented and discussed.

Comments:

This paper (and the sample simulation Optimalwash.dra found in the demo folder Samples > Optimization) will allow you to examine the financial optimization of a mill’s Brownstock washing system, taking into account cooking chemical costs, energy costs and bleach chemical costs. This model is actually a steady-state application, where only the DF is free to be adjusted. There is however no limit to the number of factors that can be included in using the OPTIMIZATION block within CADSIM Plus. This is a good candidate for application in an Operator Training System (OTS). See also, the sample drawing called ValleyOptimize.dra (also found in the Samples > Optimization folder).

The Spruce Falls Inc. mill at Kapuskasing, Ontario experiences significant disturbances to its steam production and distribution, mainly due to loss of heat recovery from its two TMP lines which results in a net loss of up to 100 t/h of steam. The lost steam must be made up quickly to avoid loss of steam header pressure that can result in paper machine production loss. However, the gas boilers experience steam drum high level problems, called priming, when attempts are made to try to ramp up their production too quickly.

As the existing steam generation system could not cope with a double loss of TMP heat recoveries, the mill was considering several alternatives for new equipment to alleviate the problem. A dynamic simulation of the steam generation, steam header, distribution network, turbines and steam users was created to test the alternatives. Each alternative was simulated to assess the operating cost and its ability to minimize the risk of production loss. The mill is acquiring a combination of these alternatives that minimizes the risk while optimizing the gas consumption with a projected pay back under two years.

Comments:

An excellent paper on the study of the dynamics of manufacturing. What happens to energy demand as rates change and the process starts up and shuts down? How does the quality change?  How might the controls be better structured to allow smoother control of upsets?

In the Spring of 1999, the Harmac Pulp Operations of Pope and Talbot Ltd. took two hog fired boilers out of service. To enable the decommissioning of these boilers, without increasing the remaining power boiler’s fossil fuel consumption, the mill had to reduce its steam consumption by approximately 60,000 lbs/hr. This paper details the approach Harmac used to achieve this goal and to ultimately reduce steam consumption by 100,000 lbs/hr. These steam savings represent a natural gas savings worth approximately $6.0 million per year.

Comments:

Note the details on "Pinch" technology referred to in this paper. It is being usedt in integrated process design to look at “chemical pinch” and “water pinch.” CADSIM Plus has a good THERMAL PINCH module for displaying graphically the Pinch Analysis results.

What are the attributes of a good pulp and paper simulator? What are the kinds of applications/problems that engineers want to solve using process simulation? Who is using simulation in the pulp and paper industries, and for what purposes? What will the future hold? This presentation addresses these simulation questions from a pulp and paper perspective. Although much of the material is based on Canadian experience, it is believed that the analysis will be fairly applicable to other regions.

The analysis presented will be a useful introduction for those looking to select suitable simulation software. Several examples demonstrate where the use of simulation resulted in significant improvements to a mill’s bottom line, which can be useful for those trying to justify the effort of doing simulation. Finally, a discussion is offered on where technological advances may take simulation in the near future.

Comments:

This is an excellent overview paper examining the value of dynamic process simulation in the paper industry today and the capabilities a good simulation system requires to fully achieve a plant’s potential.

Simulation of pulp and paper processes has become a very valuable tool to the pulp and paper industries. Whether used for predictive analysis of the consequences of process configuration changes or for process optimization by virtually changing combinations of process parameter settings, or simply for gaining better understanding of an existing process, simulation is now one of the most powerful tools for these purposes. But what can we simulate exactly? Most commercial simulators are limited to the static treatment of a subset of process variables that explicitly appear in mass and energy balance equations. Other more versatile software will also handle the time dimension (dynamic simulation) in their balance equations, providing dynamic changes of the balanced variables. Numerical methods are then used for solving the resulting differential equations pertaining to certain types of process modules such as tanks and controllers. However, an important category of process variables often used as qualitative metrics in process performance evaluation and optimization are usually absent from these models, due to the fact that they do not lend themselves to treatment in the typical mass and energy balance equations of most simulators. This article presents a potential solution to this problem and describes the development of a neural network-based module that can be independent of the classic heat and material balance of process variables upon which the model is based. One valuable application of this technology would be the prediction of paper strength and quality parameters. An example simulation that uses the newly developed module is presented and discussed.

Comments:

Neural networks are a powerful technology enabling you to use practical experience to "train" a process in your simulation, so that it responds to changed inputs just the way an experienced operator simply "knows" it will from experience.

Simulation of pulp and paper processes has become a very valuable tool to the pulp and paper industries. Whether used for predictive analysis of the consequences of process configuration changes or for process optimization by virtually changing combinations of process parameter settings, or simply for gaining better understanding of an existing process, simulation is now one of the most powerful tools for these purposes. But what can we simulate exactly? Most commercial simulators are limited to the static treatment of a subset of process variables that explicitly appear in mass and energy balance equations. Other more versatile software will also handle the time dimension (dynamic simulation) in their balance equations, providing dynamic changes of the balanced variables. Numerical methods are then used for solving the resulting differential equations pertaining to certain types of process modules such as tanks and controllers. However, an important category of process variables often used as qualitative metrics in process performance evaluation and optimization are usually absent from these models, due to the fact that they do not lend themselves to treatment in the typical mass and energy balance equations of most simulators. This article presents a potential solution to this problem and describes the development of a neural network-based module that can be independent of the classic heat and material balance of process variables upon which the model is based. One valuable application of this technology would be the prediction of paper strength and quality parameters. An example simulation that uses the newly developed module is presented and discussed.

Comments:

This is an excellent article on a highly topical cost improvement opportunity. It cites the application of integrated process design and CADSIM Plus's "Energy Pinch" module.

In the ‘90s there was great interest in North America in increasing paper recycling capacity. The enthusiasm to produce, combined with lack of experience with an emerging industry, led some to rush plant designs before they were fully vetted.

Several of the mills built during the period have since closed. The reasons for their failure include poor design, poor water management, high energy consumption and high raw material cost due to the increased demand for waste paper. Our recycle mill in Northampton, Pa. was one of those ill-fated plants. In 2001, the mill was purchased by Belkorp Industries Inc. and renamed Newstech PA, LP.

Newstech PA is a deinking pulp mill with a design capacity of 420 air-dry metric tons/day. Our engineering group has been tasked with studying the redesign and feasibility of reopening and operating the plant.

At an early stage of the process study, it was determined that some form of process simulation software should be employed. When conducting a process study, it is important not to overlook any aspect of the operation.

Comments:

A poor design and high consumption costs shut this Pennsylvania paper recycling plant down, but an in-house implementation of full, dynamic process simulation facilitated a successful restart.

The Tomlinson recovery boiler is the current conventional technology for recovering cooking chemicals and energy from black liquor. Air-blown pressurized black liquor gasification with combined cycle represents one of several gasification-based technologies that offers future promise as a replacement for the Tomlinson recovery boiler. The focus of this study is to compare air-blown gasification with combined cycle technology directly to that of the Tomlinson recovery boiler. Hence, the study scope is limited to a gasification island based on a 70 MW gas turbine and heat recovery steam generator that matches the process steam output of a Tomlinson recovery boiler for a mill producing 1300 mtpd of oven-dry pulp. Process data, needed for the performance and economic comparisons, came from simulation runs using the CADSIM Plus  process simulation platform.

Air-blown high temperature black liquor gasification with combine cycle (BLGCC) requires about 22% additional heat input to achieve the same process steam requirements as the Tomlinson recovery boiler. The air-blown BLGCC net power output is 2.5 times the output of the Tomlinson system. Air-blown BLGCC has a slight overall efficiency advantage over the Tomlinson recovery boiler (64% vs. 62% based on the simulation results presented in this paper). Preliminary economics show that air-blown pressurized BLGCC becomes more economically feasible when compared to a new recovery boiler installation. A major rebuild generally does not provide enough economic incentive for BLGCC unless the rebuild costs are substantial (>$150 per annual metric ton) or unless purchased power costs are high (>4.5¢ per kWh). In the case of a recovery boiler replacement, air-blown BLGCC can offer a very attractive incremental rate of return, although learning curve risk factors can potentially reduce this return.

Comments:

An illustration of the significant flexibility CADSIM Plus can demonstrate for simulating a wide variety of processes.

The significance of the level of carryover and breakthrough occurring across the bleach plant rotary vacuum drum washers at Australian Paper (AP) Maryvale Mill has been characterized and modeled. The model enabled the relationship between shower flow breakthrough and organic cycle up to be investigated, and a significant level of organic cycle up was found to exist in the plant. A number of alternative counter current bleach plant configurations were investigated, and an alternative configuration with a reduced level of organic cycle up was found. The alternative configuration has been implemented at Australian Paper Maryvale and has achieved a significant reduction in chlorine dioxide consumption.

Comments:

This excellent paper from staff at Nippon Paper’s Maryvale mill summarizes some really excellent process engineering work, and in particular, an unwillingness to accept that "it's always been like that, so that's the way it is!"   They examined progressive improvements that had been made over the years and how the plant runs faster now and their detective work discovered first, that the washers really weren't working well and secondly, why they weren't. Presenter Rohan Wilks paid tribute to CADSIM Plus in his paper and in the presentation, saying that it was an essential tool in their developing a quantitative understanding of what was occurring in their plant.

Chemical process simulation (CPS) software has been widely used by chemical (process) engineers to design, test, optimize, and integrate process plants. It is expected that industrial ecologists to bring these same problem-solving benefits to the design and operation of industrial ecosystems can use CPS. This paper provides industrial ecology researchers and practitioners with an introduction to CPS and an overview of chemical engineering design principles. The paper highlights recent research showing that CPS can be used to model industrial ecosystems, and discusses the benefits of using CPS to address some of the technical challenges facing companies participating in an industrial ecosystem. CPS can be used to (i) quantitatively evaluate and compare the potential environmental and financial benefits of material and energy linkages; (ii) solve general design, retrofit, or operational problems; (iii) help to identify complex and often counter-intuitive solutions; and (iv) evaluate what-if scenarios. CPS should be a useful addition to the industrial ecology toolbox.

Comments:

The paper highlights research showing that Chemical Process Simulation (CPS) can be used to model industrial ecosystems, and discusses the benefits of using CPS to address some of the technical challenges facing companies participating in an industrial ecosystem.

It has become increasingly important for industry to monitor and control energy usage, not only to reduce costs, but also to reduce the environmental footprint of their manufacturing facilities. Mill information systems provide easy access to live and historical data that can be used as the basis for enhanced decision making at both operational and managerial levels, leading to more efficient energy utilization. However, mill steam flows are routinely found to be inaccurate due to the inherent limitations of today’s measurement instrument technologies and sometimes less than optimal maintenance practices. These problems hinder any evaluation or audit of the process, and can invalidate process decisions that are meant to reduce energy usage.

This paper examines the industrial implementation of an integrated Dynamic Data Reconciliation (DDR) system that provides energy monitoring and a method of reducing gross and random errors in the measured data from a plant’s DCS. The DDR system uses process simulation to balance steam and fuel flows, optimized to give a best fit to measurement data read from the mill historian. The balanced energy flows are then put back into the plant historian at 5 minute intervals for review by operators and managers.

The implemented system provides reports that are used to compare daily usage with mill budgets. Energy reports are reviewed daily to identify problem measurements for maintenance. Reports are also used to make business decisions on fuel selection and on the generation of electricity at the extraction turbine. online displays of energy flows are updated at 5 minute intervals and are widely available to mill personnel via the mill information system.

Comments:

A practical example of how Dynamic Data Reconciliation (DDR) can be used to obtain and use critical process information to track costs and find efficiencies in day-to-day mill operations.

Quality control in complex processes such as pulping is dependent on the real-time availability and accuracy of measurements of process parameters. Instruments and mill information systems provide access to some live and historical data that can be used as the basis for process control and operator decisions. However, due to the limitations of today’s measurement instrument technologies, many key parameters may not be measured directly, and available measurements may be routinely inaccurate. These problems hinder optimal process control.

An important advancement in Dynamic Data Reconciliation (DDR) has been the use of process simulation to provide a better fit to measurement data that is read from the mill historian by, not only ensuring the fundamentals of material and energy balances, but just as importantly, by incorporating the process dynamics to allow an understanding of transient behaviors, rather than being mislead by them. This paper examines the online implementation of an integrated DDR system, which not only reduces gross and random errors in the measured data from a plant’s DCS, but which also generates valuable new information that was not previously available.

The DDR system was implemented to track the pulp from individual batch digesters at Tembec’s Specialty Cellulose Mill in Temiscaming, Quebec. Reconciled and predicted tracked values are put back into the plant historian at one minute intervals for review in near real-time. As a result, operators are able to match quality test results from the blow tank to a particular digester batch and make adjustments accordingly.

Comments:

This paper examines the online implementation of an integrated DDR system, which not only reduces gross and random errors in the measured data from a plant’s DCS, but which also generates valuable new information that was not previously available!

In this work, published experimental result data of the pulping of tagasaste (Chamaecytisus proliferus L.F.) with soda and anthraquinone (AQ) have been used to develop a model using a neural network. The paper presents the development of a model with a neural network to predict the effects that the operational variables of the pulping reactor (temperature, soda concentration, AQ concentration, time and liquid/solid ratio) have on the properties of the paper sheets of the obtained pulp (brightness, traction index, burst index and tear index). Using a factorial experimental design, the results obtained with the neural network model are compared with those obtained from a polynomial model. The neural network model shows a higher prediction precision that the polynomial model.

Comments:

The paper presents the development of a CADSIM Plus model with a Neural Network to predict the effects that the operational variables of the pulping reactor (temperature, soda concentration, AQ concentration, time and liquid/solid ratio) have on the properties of the paper sheets of the obtained pulp (brightness, traction index, burst index and tear index). Using a factorial experimental design, the results obtained with the neural network model are compared with those obtained from a polynomial model. The neural network model shows a higher prediction precision that the polynomial model.

This paper illustrates how neural network simulations can be applied to difficult-to-mathematically-describe processes.

This paper presents a model of a flotation stage using a neural network to predict the efficiency and the effect of operational parameters on the efficiency of ink removing. Two methods are used to determine the kinetic parameters of the flotation process using particular experimental conditions: experimental data obtained at a laboratory level, and simulated data by means of a neural network.

Simulated values obtained with a neural network correspond closely to the experimental results. Neural networks are long-range tools for studying processes when some knowledge of the phenomena that occur in the process is acquired in order to develop models based on the experimental results. The CADSIM Plus neural network model accurately reproduces all the effects of operation variables and can be used in a simulation of a deinking plant to determine the optimal operational conditions.