Artificial intelligence encompasses a wide range of approaches, methodologies, and techniques aimed at mimicking human intelligence in machines. In recent times, the concepts of Generative Artificial Intelligence (AI), Super AI, and Narrow AI have attracted considerable attention. Undoubtedly, the success of ChatGPT in capturing all attention has played a significant role in this. Artificial intelligence technology has a profound impact on all sectors, and sector representatives are striving to adapt to this technology more quickly. It is projected that artificial intelligence could generate an economic size of 13 trillion American dollars by 2030. Developments in artificial intelligence technologies undoubtedly lead to significant improvements in the functioning of public institutions and access for citizens. Artificial intelligence has the potential to be used in many public services, including security and defense, healthcare services, education, transportation and infrastructure, environmental and natural resource management, law and justice systems, among others. Therefore, evaluating the types of artificial intelligence, Narrow AI applications, and chatbots for public use is seen as highly beneficial from the perspective of public administration and the public sector. In our study, the topics of super artificial intelligence, generative artificial intelligence, narrow artificial intelligence, and chatbots have been extensively evaluated within the context of the public sector and public administration. Utilizing findings from both Turkish and English literature reviews, the importance and potential impacts of artificial intelligence within the public sector, along with current trends, have been comprehensively assessed. This research delves into the concepts of artificial intelligence and its subsets—super AI, generative AI, narrow AI, and chatbots—within the general framework of the public sector. China and the United States are pioneering and leading countries in terms of investment. Although the U.S. stands out in many areas regarding investment, China's integration of artificial intelligence with national strategies and its policies indicate that it may play a more dominant role in the future. There are four main implementation areas of artificial intelligence in the public sector: efficiency and automation, service delivery, data-driven governance, and ethical and regulatory challenges. A review of the literature reveals that the ethical, legal, and social implications of implementing artificial intelligence in the public sector require more careful consideration. The study makes a significant contribution to the field of artificial intelligence discussions in public administration and the public sector, providing a comprehensive assessment of current discussions on artificial intelligence in the literature.

Despite vast improvements in computational power, many large-scale optimization problems involving integer variables remain difficult to solve. Certain classes, however, can be efficiently solved by exploiting special structure. One such structure is the singly bordered block-diagonal (BBD) structure that lends itself to Dantzig-Wolfe decomposition, Lagrangian relaxation, and branch and price. We start by introducing a new measure of goodness to capture desired features in BBD structures such as granularity of the structure, homogeneity of the block sizes, and isomorphism of the blocks. We then use it to propose a new approach to identify the best BBD structure inherent in the constraint matrix. The main building block of the proposed approach is the use of a community detection methodology in lieu of graph/hypergraph partitioning methods to alleviate one major drawback of the existing approaches in the literature: predefining the number of blocks. When tested on MIPLIB2003/2010 instances and compared against the state-of-the-art technique, the proposed algorithm is found to identify very good structures and require shorter CPU time to reach comparable bounds, in most cases.

Oil refineries, producing a large variety of products, are considered as one of the main sources of air contaminants such as sulfur oxides (SO_x), hydrocarbons, nitrogen oxides (NO_x), and carbon dioxide (CO₂), which are primarily caused by fuel combustion. Gases emanated from the combustion of fuel in an oil refinery need to be reduced, as it poses an environmental hazard. Several strategies can be applied in order to mitigate emissions and meet environmental regulations. This study proposes a mathematical programming model to derive the optimal pollution control strategies for an oil refinery, considering various reduction options for multiple pollutants. The objective of this study is to help decision makers select the most economic pollution control strategy, while satisfying given emission reduction targets. The proposed model is tested on an industrial scale oil refinery sited in North Toronto, Ontario, Canada considering emissions of NO_x, SO_x, and CO₂. In this analysis, the dispersion of these air pollutants is captured using a screening model (SCREEN3) and a non-steady state CALPUFF model based on topographical and meteorological conditions. This way, the impacts of geographic location on the concentration of pollutant emissions were examined in a realistic way. The numerical experiments showed that the optimal production and pollution control plans derived from the proposed optimization model can reduce NO_x, SO_x, and CO₂ emission by up to 60% in exchange of up to 10.7% increase in cost. The results from the dispersion models verified that these optimal production and pollution control plans may achieve a significant reduction in pollutant emission in a large geographic area around the refinery site.

Many potential diversification and conversion options are available for utilization of natural gas resources, and several design configurations and technology choices exist for conversion of natural gas to value-added products. Therefore, a detailed mathematical model is desirable for selection of optimal configuration and operating mode among the various options available. In this study, we present a simulation-optimization framework for the optimal selection of economic and environmentally sustainable pathways for natural gas downstream utilization networks by optimizing process design and operational decisions. The main processes (e.g., LNG, GTL, and methanol production), along with different design alternatives in terms of flow-sheeting for each main processing unit (namely syngas preparation, liquefaction, N₂ rejection, hydrogen, FT synthesis, methanol synthesis, FT upgrade, and methanol upgrade units), are used for superstructure development. These processes are simulated using ASPEN Plus V7.3 to determine the yields of different processing units under various operating modes. The model has been applied to maximize total profit of the natural gas utilization system with penalties for environmental impact, represented by CO_2eq emission obtained using ASPEN Plus for each flowsheet configuration and operating mode options. The performance of the proposed modeling framework is demonstrated using a case study.