The Next Step for the Paper and Pulp Industry.
Along with the finesse of processing pulp into paper products comes a supply chain with levels of complex and time-demanding processes, right from harvesting & debarking to screening and bleaching of the pulp. These processes produce a large amount of data. This is where AI steps in, by transforming this data into intelligence, Artificial Intelligence has helped gain meaningful insights, predictions, and automation algorithms – taking the next step towards improved productivity, quality, and automation for the paper processing industry.
The degree of automation and intelligence in the paper and pulp industry has traditionally been high and several applications for optimizing the process has continued to increase significantly, Artificial Intelligence and its applications have also driven its way into the paper and pulp industry showing great results.
·A challenge businesses in the paper and pulp industry face are to simultaneously optimize process throughput and the quality of resulting products they find themselves adjusting optimal throughput and process settings quite often to meet the standard required for a particular product. The data collected to support such decisions is comprised of thousands of recorded high-resolution images of wood fibers, along with other sensor information.
Using AI methods to predict the pulp characteristics and properties of the final product through the analysis of the collected data, could reveal interdependencies of the process parameters and predict the service properties of the final product.
Some useful applications of AI in the paper and pulp industry include :
Predictive Asset Maintenance: Unplanned and unwanted faults and corrosions reduce the mechanical availability of all processing plants, machine learning, and AI methods can help create predictive algorithms to analyze and identify high-risk assets, factors leading to their failure, and suggest prescriptive actions before failure occurs, using appropriate time-series data of assets, variations from ideal performance can be visualized, flagged and acted upon, thus, minimizing the losses due to unplanned maintenance, resulting in overall efficiency of planning and maintaining the workforce better.
Quality Control and Output Debottlenecking: Maintenance of quality and removal of bottlenecks in production are time-consuming and labor-intensive and professional help is needed to solve the issue, AI algorithms can determine factors to produce high-quality paper and pinpoint real-time production bottlenecks faster and in a smoother way by fiding out factors hindering the quality of production such as plant ambient temperatures, Quicker debottlenecking saves time and resources which were otherwise employed in bottleneck identification and removal.
Improving Fiber Yield & Optimization of Chemicals and Energy: • Yielding of fiber from timber is an energy-consuming and hazardous process, every industry wants to use optimum amount of energy to help them increase their effectivity and efficiency. The Paper and Pulp industry also aims to achieve a higher fiber yield, AI methods can help achieve low operating costs along with minimum usage of these chemicals, and easily plan their sourcing, transport, and storage by predicting the optimum amount of energy and the minimum flow-rate of these chemicals.
As artificial intelligence continues to expand in all sectors of the economy, there are hardly any areas left where its methods and algorithms cant be put to use, keeping up with the challenges in the paper and pulp industry there is a whole new area of how AI methods can help businesses improve their efficiency.