Challenges of IoT Deployment in the Context of Developing Countries

The concept of the Internet of Things (IoT) is not exactly a novelty, even if it has not burst out yet with all the force that the market expected. The 5th generation of mobile telephony (5G) is rushing to deploy in the midst of a real trade war, and intends to get one’s own way on this and others fronts. This paper analyses how to overcome the challenges of an IoT deployment, which can be too complex to fulfil its promise of massification and ubiquity. This analysis is primarily intended to identify whether 5G will be the key to a current IoT deployment in all contexts, or whether it is wise to pursue other development paths first.

Estimation of the Amount of Electrical Energy Available From the Biogas Produced at the Faecal Sludge Treatment Plant in the City of Sokodé

The purpose of this study is to estimate the amount of energy produced from biogas at the faecal sludge treatment plant in the city of Sokodé. The methodological approach consisted in producing biogas by co-digestion of faecal sludge with the fermentable fractions of solid waste then in estimating the quantity of energy available from the produced biogas. Tests of co-digestion of faecal sludge and fermentable fractions of solid waste, showed that from 2258 tons/DM of biomass in one year, 44476 m3 of biogas, or 29177 m3 of methane could be produced. The methane content, which is 65.6 %, is a very interesting source of energy. Several techniques for producing energy from biogas exist, one of which is the production of electricity. In this study, it is a question of making the choice of an adequate electric motor which will allow to produce electric energy from the biogas on the faecal sludge treatment plant. Thus, it was necessary to estimate the quantity of energy available from the biogas produced. To do so, it was calculated the quantity of energy that can be produced by the biogas in one year, the quantity of recoverable energy produced in a year and the quantity of energy supplied by biogas in one hour. The results showed that by 2035, the co-digestion of fermentable solid waste and faecal sludge from the city of Sokodé, would produce 534,246 kWh. The recoverable part would be 507,534 kWh and the energy supplied is 58 kWh.

Positive-Unlabelled Learning based Novelty Detection for Industrial Chillers

Chiller systems are used in many different applications in both the industrial and the commercial sector. They are considered major energy consumers and thus contribute a non-negligible factor to environmental pollution as well as to the overall operating cost. In addition, chillers, especially in industrial applications, are often associated with high reliability requirements, as unplanned system downtimes are usually costly. As many studies over the past decades have shown, the presence of faults can lead to significant performance degradation and thus higher energy consumption of these systems. Thus, data-driven fault detection plays an ever-increasing role in terms of energy efficient control strategies. However, labelled data to train associated algorithms are often only available to a limited extent, which consequently inhibits the broad application of such technologies. Therefore, this paper presents an approach that exploits only a small amount of labelled and large amounts of unlabelled data in the training phase in order to detect fault related anomalies. For this, the model utilizes the residual space of the data transformed through principal component analyses in conjunction with a biased support vector machine, which can be ascribed to the concept of semi-supervised learning, or more specifically, positive-unlabelled learning.

Implementation of a Machine Tool Retrofit System

Small and medium-sized companies increasingly turning their attention towards the fourth industrial revolution. In order to increase their own long-term competitiveness, there is a growing desire to make production smarter, more efficient, safer and more sustainable through new technologies. Often, however, existing plants cannot be easily replaced by modern equipment. The reasons for this can be high investment costs, excessive downtimes or the unavailability of an equivalent machine. An alternative solution to the purchase of new equipment is the modernisation or expansion of existing systems, also called retrofitting. Thus, this paper deals with the retrofit process of a machine tool, whereby the software architecture of the control unit is the primary concern of this work.

Demand Response Model for Optimized Use of Renewable Energies in Production

A demand-response model was developed in the Automation Technology Laboratory at the Velbert/Heiligenhaus Campus (CVH) of Bochum University of Applied Sciences, in which energy users in the manufacturing sector are networked with a smart grid via a cloud platform in order to control production based on the supply of renewable energies.

Concept Towards Segmenting Arm Areas for Robot-Based Dermatological In Vivo Measurements

Dermatological in vivo measurements are used for various purposes, e.g. health care, development and testing of skin care products or claim support in marketing. Especially for the last two purposes, in vivo measurements are extensive due to the quantity and repeatability of the measurement series. Furthermore, they are performed manually and therefore represent a nonnegligible time and cost factor. A solution to this is the implementation of collaborative robotics for the measurement execution. Due to various body shapes and surface conditions, common static control procedures are not applicable. To solve this problem, spatial information obtained from a stereoscopic camera can be integrated into the robot control process. However, the designated measurement area has to be detected and the spatial information processed. Therefore the authors propose a concept towards segmenting arm areas through a CNN-based object detector and their further processing to perform robot-based in vivo measurements. The paper gives an overview of the utilization of RGB-D images in 2D object detectors and describes the selection of a suitable model for the application. Furthermore the creation, annotation and augmentation of a custom dataset is presented.

Sustainably Produced Hydrogen

The fossil fuels used to provide energy, such as coal, crude oil and natural gas, are largely responsible for carbon dioxide emissions and other so-called greenhouse gases. Hydrogen gas (H2) can make a key contribution to decarbonization. It can be produced using various processes. Several processes are available to produce hydrogen, such as (i) steam reforming, (ii) cracking process or (iii) electrolysis. Depending on the source of origin, there is a distinction made between different “colours”. Gray, blue, turquoise, yellow and green hydrogen is avail­able, the latter made using Renewable Energies. However, items such as (i) possible variants of hydrogen, (ii) leading export countries or (iii) provision paths to be preferred in the future will be discussed in this paper.

Cyber-Physical Production Systems in Settings with Limited Infrastructure

During the last decade production innovation was mainly focused on connectivity aspects. The vision of smart factories running on software, that uses collected machine data, has become true but foremost for leading industrial companies in highly developed countries. Apart from these, production can also be found in non-industrialized craft professions as well as in less developed countries. As digitalization does not necessarily require an industrial or developed setting the latter could possibly benefit from it as well. Socio-cyber-physical production systems have been used to describe the interdependencies of linked production systems but usually focus on highly developed regions as well as for industrial applications. This paper lines out similarities and differences for each case, introduces the concept of cyber-physical production systems (CPPS) and its extension to socio-CPPS (SCPPS), which emphasizes the role of human workers in the production environment. The relation between industrial, non-industrial production and innovations is examined. Furthermore, the widening of SCCPS concepts for non-industrial production is discussed.

Optimization of Biogas Production by Co-Digestion of Organic Waste (Cow Dung and Water Hyacinth)

The objective of this work is to determine the co-digestion ratio of water hyacinth and cow dung for the optimization of biogas production at Sô Ava, a lake city of Southern Benin. To achieve these ratios, we suppose that the water hyacinth has a high gas yield and cow dung ensures stability in the biodigester because it brings fresh bacteria and has a strong buffering capacity (maintenance of a stable pH). For 45 days, we have introduced a mixture of water hyacinth and cow dung in 5 mini-biodigesters of 10 liters each: digester no1 (100% of cow dung); digester no2 (100% of the water hyacinth); digester n° 3 (50% of the water hyacinth and 50% of the cow dung); digester no4 (75% of cow dung and 25% of water hyacinth); digester no5 (75% of the water hyacinth and 25% of the cow dung). The measurements of the pH, temperature and the proportion of gas (CH4, CO2, O2 and H2S) in the mini-biodigesters was done. The measurements show that the digester n° 5 produces the highest capacity of 15.24L of biogas with 70% of methane while the digester n °2 has the lowest capacity 5.47L of biogas with 58% methane. These results show that the yield of biogas produced is greater when using the mixture of the substrate with the ratio of 75% of water hyacinth and 25% of cow dung. This result encourages the energy recovery from water hyacinth, once considered as a seasonal plague which hinders navigation of local boat in the lake.

Design of an Intelligent System for Controlling and Balancing Renewable Energy Flows in an Autonomous Micro-Grid

Pooling different renewable energy sources (hydrogen, solar, wind, geothermal, etc.) enables developing a standalone energy micro-grid. The energy flows from these various sources are neither constant nor equivalent. Therefore, control and balancing mechanisms should be established for optimal energy utilization through an intelligent system based on interconnected microcontrollers networked with sensors. Our contribution addresses this issue by proposing an original architecture of an intelligent and distributed control system based on a sensor network and a strategy to share the electric power through the micro-grid. In our work we consider a micro-grid powered by sources of wind turbine, pv panels and battery which energy flows are controlled and balanced through our system depending on power demand of the loads. Alternating Current (AC) bus and Direct Current (DC) bus are tied together by an inverter. A set of microcontroller-sensor-actuators (which we named S.A.D for Sensor/Actuator Device) are deployed at strategic points in the micro-grid providing constantly data from power generated and consumed, equipment health and status. A control algorithm developed in relation to a network control strategy is implemented by combining the performance different microcontroller boards. Relying on existing literature works, a review of solution approaches to the challenging problem, of the power flows balancing between the different energy sources and storage batteries embedding appropriate IoT technologies and exploiting energy big-data platforms, is presented