A control architecture for continuous production processes based on industry 4.0: water supply systems application

Therapeutic monoclonal antibodies (mAbs) are the fastest-growing class of biotherapeutics. They are mainly used to treat cancer, inflammatory, metabolic and autoimmune diseases. Their commercial production processes are mainly based on Chinese hamster ovary (CHO) suspension cells, which are currently cultivated in fed-batch mode at cubic meter scale. The annually growing market for therapeutic mAbs and the pressure on producers to reduce their manufacturing costs have led to the increasing development of intensified and continuous production processes in recent years. Single-use systems are used in both upstream and downstream processing. This book chapter describes the main intensification approaches and operational architectures of continuous processes realized today, based on the developmental status of single-use technologies used for the up- and downstream processing of mAbs. In this context, the terms “process intensification” and “continuous process” are defined, while the preferential application of single-use systems is described using literature, and further by own studies. Based on the findings, the main challenges for the implementation of intensified and continuous mAb production using single-use systems are identified.

Continuous production processes are often highly complex and involve machine failures as well as unscheduled process downtimes. Failures result in the production of waste and in high opportunity costs, but their causes are not always apparent to machine operators. As a result, identifying failure root causes and avoiding risky process states is of high interest for producers. This work presents an approach for a data-driven failure risk assessment that is validated on real-world process data of a nonwovens production line. In this approach, association rule mining is adapted to continuous processes for producing highly interpretable results in the form of association rules that represent the main causes for failures. The methodology includes data preparation, modelling of production states and the evaluation of root causes using an associative classification algorithm. The result of this paper is a method for an interpretable risk assessment in continuous production processes. By using the method in live production, causes of failures can be detected and interpreted. The universal structure of the developed method supports applications in many other continuous production processes.

In continuous production process, there are many complex equipments and special requirements such as continuity and stability, which are very difficult to be presented in classical MINLP formulations. So the production scheduling for continuous process is more difficult than that for discrete process or batch process. In continuous process, experience rules usually play very important roles. But it is impossible to obtain the optimal solution just according to the rules. A novel generalized disjunctive programming model for multi-periodic continuous process scheduling is proposed. In this model, some logistic constraints and experience rules are represented in disjunctive forms, which make the model more completely. An example is used to illustrate the effectiveness of this method.

Continuous annealing production process generally consists of multiple complex processes that are coupled to each other, and each process contains many control variables. It is difficult to establish a precise mechanism model of the production process. The operators mainly set these control variables based on past production experience, which often result in great fluctuations of product quality (even unqualified products) and high energy consumption. This in turn significantly affected production cost and economic benefits of the cold rolling mill. To efficiently handle this problem, an ensemble learning modeling method based on production data is first proposed for this production process, and then, a multiobjective operation optimization model is established to optimize the operation of continuous annealing production process. Finally, an improved multiobjective differential evolution algorithm based on search process memory is developed to solve this model and achieve the optimal setting of control variables. The computational results on both benchmark problems and practical problems illustrate that the proposed algorithm is superior to some powerful multiobjective evolutionary algorithms in the literature and it can effectively achieve good setting of control variables for the continuous annealing production process.

Continuous biodiesel production on laboratory and industrial scale was analyzed, with focus on their advantages and disadvantages. Attention was paid to specific characteristics of industrial processes in order to point out the advanced technologies. The well-known base-catalyzed continuous biodiesel production processes are related to problems caused by the immiscibility of the reactants (alcohol and oil), application of relatively high operating temperature (usually the boiling temperature of alcohol or one near it) and obtained yield of methyl ester yields lower than desired. One way to overcome these problems is to employ special reactor design favoring the emulsion process and increasing the overall rate of biodiesel production process, even at room temperature and atmospheric pressure. The second way is to apply heterogeneous catalysts in continuous processes, which will probably be the optimal approach to economically justified and environmentally friendly biodiesel production.

Life cycle assessment (LCA) of a solar selective surface produced by continuous process and solar flat collectors

Climate change, water scarcity and pollution, and growing water demand across all sectors are stressing existing water supply systems, highlighting the need for alternative water supply (AWS) systems. AWS systems are those that have not typically existed in the traditional supply portfolio of a given service area but may be used to reduce the pressure on traditional water resources and potentially improve the system’s resilience. AWS systems have been used for decades, often where traditional systems are unable to maintain sufficient quantity and quality of water supply. Simpler forms of AWS systems, like rainwater harvesting, have been used for centuries. As human population and water demand have increased, AWS systems now play a larger role in the broader supply portfolio, but these systems alone are not able to fully resolve the increasingly complex mix of problems contributing to water stress. Entrenched challenges that go beyond technical issues include low institutional capacity for developing, operating, and maintaining AWS systems; monitoring water quality; more efficiently using available resources; and establishing clear responsibilities among governments, service providers, and property owners. Like traditional water supply systems, AWS systems should be developed within a sustainability-focused framework that incorporates scenario planning to account for evolving natural and institutional conditions. In ASEAN, the adoption of AWS systems varies among countries and provides context-specific lessons for water management around the world. This article provides an overview of AWS systems in the region, including rainwater harvesting, graywater recycling, wastewater reclamation, desalination, and stormwater harvesting.

This chapter addresses the challenges of evaluating the business case for continuous manufacturing of pharmaceuticals, looking beyond traditional technical assessments made at the unit operations or individual production facility level. It provides an overview of key concepts, approaches, and tools for the early assessment of supply network configuration opportunities enabled by continuous production processing interventions. Multiple levels of analysis are considered with the aid of examples based on major UK research programs on continuous production process technologies. Particular emphasis is placed on the potential for achieving enhanced product flexibility (in terms of volume and variety) and, depending on scale, the optimum number and location of manufacturing operations to support speed to market and system-level cost benefits. In the case of multiple manufacturing operations using continuous production process technologies, where production facility replication through digital twins is becoming a key enabler, the chapter sets out a supply network design and analysis approach that evaluates the commercial and operational viability of alternative manufacturing supply network scenarios.

High-cost production of bioplastics polyhydroxyalkanoates (PHA) is a major concern for their large scale application. In order to produce PHA economically, new technology must be developed to reduce costs on energy consumption, fresh water and substrate usages. It is also important to conduct the PHA production process in a continuous way rather than in a batch process. A halophile Halomonas campaniensis strain LS21 was isolated to allow the development of a sea water based open and continuous process for PHA production utilizing mixed substrates consisting of mostly cellulose, starch, lipids and proteins. To study the feasibilities of open and long-term cultivation as well as genetic manipulation of this strain, polyhydroxybutyrate (PHB), the first member of the diverse PHA family, was taken as an example for the application of H. campaniensis LS21 in a robust and long lasting fermentation process. Wild type and recombinant H. campaniensis LS21 containing a PHB synthesis genes phbCAB were allowed respectively to grow in artificial seawater containing mixed substrates similar to kitchen wastes, including soluble and insoluble cellulose, proteins, fats, fatty acids and starch for 65 days without interruption. In the presence of 27 g/L NaCl under a pH around 10 at 37°C, the recombinant produced approximately 70% PHB and the wild type 26% during the 65 days fermentation process without infection. H. campaniensis LS21 secreted extracellular amylase, lipase, protease and cellulase simultaneously during the whole process to allow consumption of the mixed substrates. The recombinant was also found to stably maintain the phbCAB plasmid over the entire 65 days process. The seawater based open and continuous process based on halophilic Halomonas campaniensis LS21 allowed the applications of kitchen wastes like mixed substrates as nutrients for production of bioplastic PHB. This study demonstrates the advantages of this technology in terms of energy saving (non-sterilization), seawater based (not fresh water needed), long-lasting and continuous open processing (against batch process), and low cost substrates (non-food mixed substrates). Combined with its ease of genetic manipulation, Halomonas campaniensis LS21 could be developed into a platform for low cost production of chemicals, materials and biofuels.

This paper presents a tentative analysis method for unreplicated factorial designs where regular statistical experimental analysis cannot be used. The methodology is demonstrated through the analysis of an unreplicated two-level, two-factor factorial experiment performed in a continuous production process where the process was not in statistical control and where changes in the experimental design made conventional experimental analysis impossible. The first step of the analyses included screening of the sampled data. Principal component analysis and factor analysis were then used to create an overview of how the various responses and experimental factors were related. Carbon monoxide efficiency was selected as the most important parameter to be analysed further. Elastic net regression was used as a screening tool to remove non-significant factors, interaction, and covariates. Finally, the carbon monoxide efficiency variation was modelled using an intervention analysis. Two experimental factors were found to actively influence the response. The experiment that from other perspectives can be considered to be unanalysable, did thus reveal causal effects. The results imply that for processes where the process dynamics may be monitored, observations of the process dynamics may reduce the needs for repeated experimental runs, thus reducing the experimental costs.

High-tensile joints of continuously fusion bonded hybrid structures

Analyzing variability in continuous processes

Plantwide Optimizing Control of a continuous bioethanol production process

Compact and efficient continuous mixing processes for production of food and pharmaceutical powders

Non-invasive biosensors have attracted attention for their potential to obtain continuous, real-time physiological information through measurements of biochemical markers, such as one of the most important-glucose, in biological fluids. Although some optical sensing materials are used in non-invasivedevices for continuous glucose monitoring (CGM), surface or localized plasmon sensing material are seldom applied in CGM owing to modest sensitivity and bulk sensing apparatus. Herein, a metasurface (MGMSPR) biosensor based on the metasurface plasmon resonance chip modified with heterostructured Ti3C2 MXene-Graphene oxide (MG) is reported, which potentially enables ultra-sensitive glucose detection. The sensor consists of a dual-channel microfluidic device integrated with silver mirror enhanced MGMSPR chips. Not only does it promote the entry of glucose oxidase (GOD) into the internal pores and enhance the stable fixation of GOD in the membrane, but also the integration of MG material provides a high specific surface area and unique electronic properties, thereby significantly enhancing the sensitivity of the MGMSPR sensor. The detection limit of MGMSPR biosensor is 106.8µM. This pioneering approach opens new avenues for monitoring physiological parameters and process analytical technology on an optical platform, providing continuous health monitoring and production process control through optical sensors.