It is widely accepted that reducing process variability will improve product quality. However, unless the mill is able to sell the improved product for a premium or can secure more sales the economic benefits of reduced product variability may be limited and/or difficult to quantify.

The reduction in process / product variability does present opportunities for substantial economic benefit through production rate increases or operating cost reduction. These opportunities for economic benefit are mill specific and understanding the mill economic drivers and area production limitations is required. It is often the case that integrated mills will have different benefits than stand alone operations.

This article focuses on quantifying the economic benefits from reducing cyclic variability and process upset variability.

In this case, the process oscillates continuously. The causes of cyclic variability are too numerous to discuss here, but range from process design issues to poor controller tuning. Appreciating the benefits from reducing cyclic process variability is a two-step process. The first step is to reduce variability in key process variables. The second step is to shift the operating targets.

Reducing cyclic variability may enable an overall shift in the production rate as shown in the dryer limited pulp machine example below. Decreasing the BW variability from the red line to the blue line allows a Basis Weight target shift to the green line. The increased production rate may represent the most important economic benefit.


Cyclic variability often results in the over application of chemicals, energy or high-cost fibre. Reducing cyclic variability can therefore decrease operating costs, whether the variability originates in the raw material process source (i.e. chemical flow variability, steam pressure variability) or from an external process disturbance that is causing product variability.

In the example shown below, the paper machine moisture variability is caused by variability in the wet end. Reducing the wet end variability reduces the moisture variability from the red line to the blue line. This allows a moisture target shift (green line) to the upper moisture specification that will reduce dryer energy consumption.


In the example below, reducing the ClO2 variability from the red line to the blue line will allow the operator to target shift the ClO2 dosage (to the green line) while still achieving the brightness target.


The economic benefits of reducing the process variability can be determined by estimating conservatively the amount of the target shift and calculating the associated reduction in chemical or energy costs.

There are no set formulas for calculating the economic benefits of reducing process variability for all mills. The benefits must be evaluated on a mill-by-mill basis.

The mill economic drivers must first be understood. The key process variables that affect these drivers must then be assessed. Reducing the variability in these key process variables will often allow target shifting to occur, which in turn will result in production rate increases or a reduction in operating costs.

The ultimate objectives of a ProNamics optimization surveys is to reduce operating costs (raw material, chemical and energy costs) and increase production rate as well as increasing operational efficiency and improving product quality. These objectives will be achieved by reducing process variability in the key process variables. A ProNamics Control Inc. optimization survey will provide a clear path forward for achieving these objectives.

High-speed data acquisition equipment is used to collect process data directly from the key process variables such as the BW gauge and the control systems I/O. Time series analysis tools such as the power spectrum and cross correlation are applied to assist in diagnosing variability problems Coupling tests are conducted to quantify the impact of the source on product quality. Many open loop bump tests are conducted to define the loop process dynamics and provide insight into control valve non-linearity and loop design problems. Controllers are retuned where appropriate to improve process stability. Process simulation tools are applied where appropriate to diagnose variability problems and define the optimum recommendations.

The professionals at ProNamics have been conducting pulp mill and paper machine optimization surveys in the pulp and paper industry for many years. Their extensive knowledge of process equipment, control strategy alternatives, control loop health and process simulation techniques allow them to focus on the real problem areas and develop authoritative recommendations.

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