The market volatility in the oil and gas (O&G) sector, the dwindling demand for oil due to the impact of COVID-19, and the push for alternative greener energy are driving the need for innovation and digitization in the O&G industry. This has attracted research interest from academia and the industry in the application of industry 4.0 (I4.0) technologies in the O&G sector. The application of some of these I4.0 technologies has been presented in the literature, but the domain still lacks a comprehensive survey of the application of I4.0 in the O&G upstream sector. This paper investigates the state-of-the-art efforts directed toward I4.0 technologies in the O&G upstream sector. To achieve this, first, an overview of the I4.0 is discussed followed by a systematic literature review from an integrative perspective for publications between 2012–2021 with 223 analyzed documents. The benefits and challenges of the adoption of I4.0 have been identified. Moreover, the paper adds value by proposing a framework for the implementation of I4.0 in the O&G upstream sector. Finally, future directions and research opportunities such as framework, edge computing, quantum computing, communication technologies, standardization, and innovative areas related to the implementation of I4.0 in the upstream sector are presented. The findings from this review show that I4.0 technologies are currently being explored and deployed for various aspects of the upstream sector. However, some of the I4.0 technologies like additive manufacturing and virtual reality are least explored.

A dual-band coplanar-waveguide (CPW)-fed transparent antenna for active radio-frequency identification (RFID) tags working at 2.45 GHz and 5.8 GHz is proposed. AgHT-8 thin film, includes the silver-coated polyester as the conductive layer, and polyethylene terephthalate as the substrate, is used to design and fabricate the antenna with high transparency of above 80%, which is sufficient to provide the ability to laminate the surface of the tag devices with little chance of detection. The overall dimension size of the proposed design is 36 ×39 ×0.175 mm ³ . The measured bandwidth of 59.34% (1.6 to 2.95 GHz) and 16.95% (5.4 to 6.4 GHz) is achieved for the lower band and the upper band, respectively. The proposed antenna has considerably less thickness and a lighter structure compared with the nontransparent designs.

Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been implemented in a wide area of combinatorial optimization problems. Metaheuristic algorithms have been evolved and modified with respect to the problem nature since it was recommended for the first time. As there is a growing interest in incorporating necessary methods to develop metaheuristics, there is a need to rediscover the recent advancement of metaheuristics in combinatorial optimization. From the authors’ point of view, there is still a lack of comprehensive surveys on current research directions. Therefore, a substantial part of this paper is devoted to analyzing and discussing the modern age metaheuristic algorithms that gained popular use in mostly cited combinatorial optimization problems such as vehicle routing problems, traveling salesman problems, and supply chain network design problems. A survey of seven different metaheuristic algorithms (which are proposed after 2000) for combinatorial optimization problems is carried out in this study, apart from conventional metaheuristics like simulated annealing, particle swarm optimization, and tabu search. These metaheuristics have been filtered through some key factors like easy parameter handling, the scope of hybridization as well as performance efficiency. In this study, a concise description of the framework of the selected algorithm is included. Finally, a technical analysis of the recent trends of implementation is discussed, along with the impacts of algorithm modification on performance, constraint handling strategy, the handling of multi-objective situations using hybridization, and future research opportunities.

A Markov chain is commonly used in stock market analysis, manpower planning, and in many other areas because of its efficiency in predicting long run behavior. However, the Air Quality Index (AQI) suffers from not using a Markov chain in its forecasting approach. Therefore, this paper proposes a simple forecasting tool to predict the future air quality with a Markov chain model. The proposed method introduces the Markov chain as an operator to evaluate the distribution of the pollution level in the long term. Initial state vector and state transition probability were used in forecasting the behavior of Air Pollution Index (API) that has been obtained from the observed frequency for one state shift to another. The study explores that regardless of the present status of API, in the long run, the index shows a probability of 0.9231 for a good state, and a moderate and unhealthy state with a probability of 0.0722 and 0.0037, while for very unhealthy and hazardous states a probability of 0.0001 and 0.0009. The outcome of this study reveals that the model development could be used as a forecasting method that able to help government to project a prevention action plan during hazy weather.

Land and forest fires especially in Riau Province, Indonesia, have affected the length and breadth of Indonesia. The fires are normally hampered by seasonal dry conditions such as El Nino effect. In addition, the haze has affected the neighboring countries such as Malaysia, Singapore and south of Thailand. The effects of haze on human health as reported in that particular year were about 20 million people have suffered from respiratory problems and serious deterioration in overall health. There were other effects on environment, economy, flora and fauna in Southeast Asia region due to this disaster. This research proposes to develop a smart monitoring system using Long Range Wide Area Network (LoRa WAN) with low power wireless data communication and Internet of Things (IoT) technology. With LoRa technology, data can be transmitted up to 30 miles which is worthwhile to cover some of Riau Province that have been badly impacted by this disaster. In this article propose to develop sensors system that capable of detecting land and forest fire. The sensors will be located at several locations that has badly impacted previously. LoRa IoT Technology will be deployed to provide a platform for connecting the sensors. An early indication of land or forest fires is vital for quick prevention before they become uncontrollable and overwhelming. The design and development of LoRa sensors give high feasibility to overcome current issues in Riau Province because of land and forest fire.