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Adsorption parameters (e.g. Langmuir constant, mass transfer coefficient and Thomas rate constant) are involved in the design of aqueous-media adsorption treatment units. However, the classic approach to estimating such parameters is perceived to be imprecise. Herein, the essential features and performances of the ant colony, bee colony and elephant herd optimisation approaches are introduced to the experimental chemist and chemical engineer engaged in adsorption research for aqueous systems. Key research and development directions, believed to harness these algorithms for real-scale water treatment (which falls within the wide-ranging coverage of the Sustainable Development Goal 6 (SDG 6) ‘Clean Water and Sanitation for All’), are also proposed. The ant colony, bee colony and elephant herd optimisations have higher precision and accuracy, and are particularly efficient in finding the global optimum solution. It is hoped that the discussions can stimulate both the experimental chemist and chemical engineer to delineate the progress achieved so far and collaborate further to devise strategies for integrating these intelligent optimisations in the design and operation of real multicomponent multi-complexity adsorption systems for water purification.

This chapter provides an overview of the potential use of Intelligent Transport Systems (ITS) and associated artificial intelligence (AI) techniques in the land transport sector in an attempt to achieve related United Nations Sustainable Development Goals (SDGs) targets. ITS applications that have now been extensively tested worldwide and have become part of the everyday transport toolkit available to practitioners have been discussed. AI techniques applied successfully in specific ITS applications such as automatic traffic control systems, real-time image processing, automatic incident detection, safety management, road condition assessment, asset management and traffic enforcement systems have been identified. These methods have helped to provide traffic engineers and transport planners with novel ways to improve safety, mobility, accessibility and efficiency in the sector and thus move closer to achieving the various SDG targets pertaining to transportation.

The Internet of Things (IoT) is a cloud system that saves energy by being involved in the decision-making process of machines. By this means, machines create an environment of direct interaction without the need for explicit instructions. In this chapter, answers have been sought to the questions of what kind of research has been done on IoT-based technological devices, and how IoT-based technologies are effective in sustainable food production. The systematic literature review examined the scientific research on IoT and Food in the range of 2010–2022 in Scopus. The general framework of the research has been carried out in the context of 6Ws (who, when, where, what, why, and how), which is one of the systematic literature reviews. The results obtained have been analyzed and interpreted in the MAXQDA 2020 qualitative data analysis program. In the findings obtained, it has been determined that IoT and food have gained importance worldwide, especially in England, India, and China. Furthermore, it has been determined that most of the studies on IoT are based on case studies, and all the articles examined are collected in three main focus points. Subcodes have been created under the main codes ‘Food Supply Chain, Smart and Sustainable Agriculture ve Waste Management’, and problem points have been tried to be customized.

To operate with high efficiency and minimise the risks of power failures, power systems require careful monitoring. The availability of real-time data is crucial for assessing the performance of the grid and assisting operators in gauging the present security of the grid. Traditional supervisory control and data acquisition (SCADA)-based systems actually employed provides steady-state measurement values which are the calculation premise of State Estimation. More often, however, the power grid operates under dynamic state and SCADA measurements can lead to erroneous and inaccurate calculation results. The introduction of the phasor measurement unit (PMU) which provides real-time synchronised voltage and current phasors with very high accuracy is universally recognised as an important aspect of delivering a secure and sustainable power system. PMUs are a relatively new technology and because of their high procurement and installation costs, it is imperative to develop appropriate methodologies to determine the minimum number of PMUs as well as their strategic placements to guarantee full observability of a power system. Thus, the problem of the optimal PMU placement (OPP) is formulated as an optimisation problem subject to various constraints to minimise the number of PMUs while ensuring complete observability of the grid. In this chapter, integer linear programming (ILP), genetic algorithm (GA) and non-linear programming (NLP) constrained models of the OPP problem are presented. A new methodology is proposed to incorporate several constraints using the NLP. The optimisation methods have been written in Matlab software and verified on the standard Institute of Electrical and Electronics Engineers (IEEE) 14-bus test system to authenticate their effectiveness. This chapter targets United Nations Sustainable Development Goal 7.