Assessing the value of information of data-centric activities in the chemical processing industry 4.0

A new process analytical technology soft sensor based on electrical tomography for real-time monitoring of multiphase systems

BACKGROUND

Electrical tomography is widely recognized for its high time resolution and low cost. However, the implementation of electrical tomographic solutions has been hindered by the high computational overhead associated, which causes delays in the analysis, and numerical instability, that results in unclear reconstructed images. Therefore, it has been mostly applied offline, for qualitative tasks and with some delay. Applications requiring fast response times and quantification have been hindered or ruled out.

RESULTS

In this article, we propose a new process analytical technology soft sensor that maps directly electrical tomography signals to the relevant parameter to be monitored. The data acquisition and estimation steps occur almost instantaneously, and the final accuracy is very good (R2 = 0,994).

SIGNIFICANCE AND NOVELTY

The proposed methodology opens up good prospects for real-time quantitative applications. It was successfully tested on a pilot piping installation where the target property is the interface height between two immiscible fluids.

The readiness of manufacturing industries to transit to Quality 4.0

Purpose

This research provides the essential aspects of the transition from traditional quality methods like total quality management, quality assurance and quality control to a new quality approach linked with the Industry 4.0 era. The purpose of this paper is to address this issue.

Design/methodology/approach

The study used a survey method to obtain the practitioners' perceptions of the Quality 4.0 (Q4.0) concepts. Both closed-ended and open-ended structured questionnaires assessed the perceptions of respondents regarding manufacturers' readiness and Q4.0 awareness to transition to Q4.0. Non-probability and purposive sampling tactics selected 15 Tanzanian manufacturing industries (TMIs). Garnered data were scrutinised quantitatively and qualitatively utilising Minitab® 20, SmartPLS 3.3.7 and MAXQADA 2020 software packages.

Findings

The results indicate that TMIs are equipped to deploy the Q4.0 approach because industrialists are familiar with the concept's characteristics and dimensions and the benefits of implementing Q4.0. Most TMIs utilise a Q3.0 method for managing quality, while some manufacturing industries have begun to apply Q4.0 leveraging technologies. The study revealed several factors influencing Q4.0 readiness in TMIs, including leveraged technology adoption, training, Q4.0 skills, infrastructures, the government set-up, top management support, Q4.0 strategy and vision, collaboration, awareness, knowledge of Q4.0, customer and supplier centeredness and organisational culture.

Research limitations/implications

The implication of this study is on Q4.0 awareness creation so that industries can grab the advantages of Q4.0 leveraged technologies. Another implication is that organisational readiness factors identified in this study are critical for the effective adoption of Q4.0. The highlighted influences may be utilised as indications to determine an organisation's readiness to transition to the Q4.0 approach. This research was limited to TMIs, excluding service firms, mining, and the building and construction industry due to differences in their mode of operation.

Originality/value

Determining readiness factors and awareness for the Q4.0 study is probably the first amongst the seven East African countries, including Tanzania. This study thus bridges a huge gap in fulfilling the need of this research type.

Linear and Non-Linear Soft Sensors for Predicting the Research Octane Number (RON) through Integrated Synchronization, Resolution Selection and Modelling

The Research Octane Number (RON) is a key quality parameter for gasoline, obtained offline through complex, time-consuming, and expensive standard methods. Measurements are usually only available a few times per week and after long delays, making process control very challenging. Therefore, alternative methods have been proposed to predict RON from readily available data. In this work, we report the development of inferential models for predicting RON from process data collected in a real catalytic reforming process. Data resolution and synchronization were explicitly considered during the modelling stage, where 20 predictive linear and non-linear machine learning models were assessed and compared using a robust Monte Carlo double cross-validation approach. The workflow also handles outliers, missing data, multirate and multiresolution observations, and processes dynamics, among other features. Low RMSE were obtained under testing conditions (close to 0.5), with the best methods belonging to the class of penalized regression methods and partial least squares. The developed models allow for improved management of the operational conditions necessary to achieve the target RON, including a more effective use of the heating utilities, which improves process efficiency while reducing costs and emissions.

Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective

In Industry 4.0, smart manufacturing is facing its next stage, cybermanufacturing, founded upon advanced communication, computation, and control infrastructure. Cybermanufacturing will unleash the potential of multi-modal manufacturing data, and provide a new perspective called computation service, as a part of service-oriented architecture (SOA), where on-demand computation requests throughout manufacturing operations are seamlessly satisfied by data analytics and machine learning. However, the complexity of information technology infrastructure leads to fundamental challenges in modeling and analysis under cybermanufacturing, ranging from information-poor datasets to a lack of reproducibility of analytical studies. Nevertheless, existing reviews have focused on the overall architecture of cybermanufacturing/SOA or its technical components (e.g., communication protocol), rather than the potential bottleneck of computation service with respect to modeling and analysis. In this paper, we review the fundamental challenges with respect to modeling and analysis in cybermanufacturing. Then, we introduce the existing efforts in computation pipeline recommendation, which aims at identifying an optimal sequence of method options for data analytics/machine learning without time-consuming trial-and-error. We envision computation pipeline recommendation as a promising research field to address the fundamental challenges in cybermanufacturing. We also expect that computation pipeline recommendation can be a driving force to flexible and resilient manufacturing operations in the post-COVID-19 industry.

Multiscale and Multi-Granularity Process Analytics: A Review

As Industry 4.0 makes its course into the Chemical Processing Industry (CPI), new challenges emerge that require an adaptation of the Process Analytics toolkit. In particular, two recurring classes of problems arise, motivated by the growing complexity of systems on one hand, and increasing data throughput (i.e., the product of two well-known “V’s” from Big Data: Volume × Velocity) on the other. More specifically, as enabling IT technologies (IoT, smart sensors, etc.) enlarge the focus of analysis from the unit level to the entire plant or even to the supply chain level, the existence of relevant dynamics at multiple scales becomes a common pattern; therefore, multiscale methods are called for and must be applied in order to avoid biased analysis towards a certain scale, compromising the benefits from the balanced exploitation of the information content at all scales. Also, these same enabling technologies currently collect large volumes of data at high-sampling rates, creating a flood of digital information that needs to be properly handled; optimal data aggregation provides an efficient solution to this challenge, leading to the emergence of multi-granularity frameworks. In this article, an overview is presented on multiscale and multi-granularity methods that are likely to play an important role in the future of Process Analytics with respect to several common activities, such as data integration/fusion, de-noising, process monitoring and predictive modelling, among others.

The Paradigms of Industry 4.0 and Circular Economy as Enabling Drivers for the Competitiveness of Businesses and Territories: The Case of an Italian Ceramic Tiles Manufacturing Company

Sustainable development and the circular economy are two important issues for the future and the competitiveness of businesses. The programs for the integration of sustainability into industrial activities include the reconfiguration of production processes with a view to reducing their impact on the natural system, the development of new eco-sustainable products and the redesign of the business model. This paradigm shift requires the participation and commitment of different stakeholder groups and industry can completely redesign supply chains, aiming at resource efficiency and circularity. Developments in key ICT technologies, such as the Internet of Things (IoT), help this systemic transition. This paper explores the phases of the transition from a linear to a circular economy and proposes a procedure for introducing the principles of sustainability (environmental, economic and social) in a manufacturing environment, through the design of a new Circular Business Model (CBM). The new procedure has been tested and validated in an Italian company producing ceramic tiles, using the digitalization of the production processes of the Industry 4.0 environment, to implement the impact assessment tools (LCA—Life Cycle Assessment, LCC—Life Cycle Costing and S-LCA—Social Life Cycle Assessment) and the business intelligence systems to provide appropriate sustainability performance indicators essential for the definition of the new CBM.